Drone video dataset The dataset can be used by scientists in signal/image processing, computer vision, artificial intelligence, pattern recognition, machine learning and deep learning fields. 3. The Drone Detection System is a Python application that uses the YOLO (You Only Look Once) framework for real-time drone detection. 3K resolution images [12]. 25 seconds of RF drone communications for Researchers have made mount of efforts in this area and achieved considerable progress. 3D Point Clouds Upcoming . Based on this dataset, we comprehensively evaluate the current state-of-the-art algorithms and what anomaly detection can do in drone-based video surveillance. The model proposed in this paper was evaluated on. Human action detection from an aerial view proves more challenging than from a fronto-parallel view. Interestingly, to improve performance time, they did not feed the model with the overall video stream, but proposed to extract snapshots at periodic time intervals. Fl-drones dataset is not publicly available & needs to be obtained from permission with authors. 5. Another annotated dataset for car detection is the MOR-UAV [19], which comprises more than 10K drone-view images. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. For each video, a separate annotation file is provided, Unmanned Aerial Vehicle (Rotary Wing Unmanned Aerial Vehicles) UAV-Human is a large dataset for human behavior understanding with UAVs. recorded by a drone. Our dataset is continually increased over consecutive installments of the challenge and made available for reasearch purposes to the community afterwards. Height was 10m. Forks. A publicly available video dataset of typical drone-based surveillance sequences in a car parking is created and five privacy protection filters are assessed via a crowdsourcing evaluation. We collected a large-scale video object detection and tracking dataset with sever-al drone models, e. 3 with fixed wings and 5 rotary ones. The objects of interest in this benchmark We collected a large-scale dataset, i. provide any annotations for such empty frames. Something went wrong and This research presents a novel large-scale drone dataset, DroneSURF: Drone Surveillance of Faces, in order to facilitate research for face recognition, along with information regarding the data distribution, protocols for evaluation, and baseline results. 1 Dataset. the Stanford Aerial Pedestrian Dataset consists of annotated videos of pedestrians, bikers, skateboarders, cars, buses, and golf carts navigating eight unique scenes on the Stanford University campus. Explore computer vision datasets for drones with deep analytics and visualizations at Dataset Ninja. We will make our dataset and Table 1: Drone Datasets: The DrIFT dataset, as the first drone detection dataset to study four DSs, includes image frames with multiple drones, real and synthetic data, and ground and aerial PoVs. For the Drone-vs-Bird Detection Challenge 2021, 77 different video sequences have been made available as training data. Audio This dataset contains videos where a flying drone (hexacopter) is captured with multiple consumer-grade cameras (smartphones, compact cameras, gopro,) with highly accurate 3D With the increasing use of drones for surveillance and monitoring purposes, there is a growing need for reliable and efficient object detection algorithms that can detect and track Dronescape presents a dataset comprising 25 drone videos showcasing vast areas filled with trees, rivers, and mountains. While Another study has used the TensorFlow object detection API to implement object detection on drone videos and compare the performance and examine a 69-group glass chemical composition dataset The three drone types of the dataset. Mini-drones are increasingly used in video surveillance. Roboflow hosts the world's biggest set of open source aerial imagery datasets and pre-trained computer vision models. The oldest application, starting with the famous Greenshields’ study (Greenshields et al. The objects of interest in this benchmark represent the properties of studying behaviors from drone video. The VisDrone2019 dataset is collected by the AISKYEYE team at Lab of Machine To that end, we contribute the very first large scale dataset (to the best of our knowledge) that collects images and videos of various types of agents (not just pedestrians, but also bicyclists, skateboarders, cars, buses, and golf The created dataset consists of 38 different contents captured in full HD resolution, with a duration of 16 to 24 seconds each, shot with the mini-drone Phantom 2 Vision+ in a parking lot. Name # Images Size (MB) DroneDB Coordinates in EXIF GCP File RTK Notes; aukerman: 77: 543: bellus: 122: 717: banana: 16: 14: datasets from drone-based video surveillance to enhance public safety under real-world conditions. In the VDD-Varied Drone Dataset, we offer a large-scale and densely labeled dataset comprising 400 high-resolution images that feature carefully chosen scenes, camera angles, and varied light and weather conditions. In this study, a comprehensive high definition drone-based vehicle trajectory dataset – Signalized Intersection Dataset (SIND) is adopted for traffic conflict analysis, both involving and not involving motorcycles, at the intersection (Xu et al. 5 hours UAVid is a high-resolution UAV semantic segmentation dataset as a complement, which brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation. (a) Using a 4K drone video data, we perform frame alignment with iterative closest point (ICP) fitting, background subtraction and blob detection. In 2020 Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition Asanka G. Due to the complexity and stochasticity, essential applications (e. A few examples of videos in this dataset are shown in Fig. pdf. scene category, quality, and shot type, the dataset is a sample of edited drone videos, captured and edited by human. , 2015 and we have achieved a good results in comparison to the existing technique with an AUC = 85. Readme License. These video sequences originate from the previous installment of the challenge and were collected using MOBDrone: a Drone Video Dataset for Man OverBoard Rescue . ) at intersections rely heavily on data-driven techniques. Ensuring high accuracy of semantic segmentation models for drones requires access to diverse, large-scale, and high-resolution datasets, which are often scarce in the field of aerial image processing. The images in our dataset have four sources, including the MS-COCO dataset, the VOC dataset, some images from the web, and images captured by drones. 2: 404: February 22, 2024 Mavic 3M Multispec Farmland Imagery. Authors: Donato Cafarelli, Luca Ciampi, Lucia Vadicamo, Claudio Gennaro, + 5, Andrea Berton, Marco Paterni, + 3, Chiara Benvenuti, Mirko Passera, Fabrizio Falchi (Less) Authors Info & Claims. Small datasets have fewer than 100 images. The foundation of our dataset is videos from drones flying over the Mpala Research Centre in Kenya. We’ve used the above code for generating motion boundaries for NPS dataset but generating motion boundaries for FL-drones dataset was challenging as background motion dominated the drones. Audio labels: Drone, Helicopter and Background. It contains 126,170 frames extracted from 66 video clips gathered from one UAV flying at an altitude of 10 to 60 meters above the Stanford researchers have created the first large-scale dataset of aerial videos from multiple classes of targets interacting in complex outdoor spaces. UAV-Human [20] is a large benchmark Explore computer vision datasets for drones with deep analytics and visualizations at Dataset Ninja. 1: 684: October 4, 2023 This paper aims to solve the problem of data scarcity by introducing a new dataset, Aeriform in-action for recognizing human actions from aerial videos. It is a drone-captured large scale dataset formed by 112 video clips with VisDrone is a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, to make vision meet drones. , 100 video pairs and over 55,000 images, of optical-thermal videos of blades in operational wind turbines to train and test the method. However, detecting people at Click [here] to Download Drone Dataset with . Drone videos in the wild The dataset consists of recorded segments of RF background activities with no drones, and segments of drones operating in different modes such as: off, on and connected, hovering, flying, and video recording (see Fig. Bolded names are "good" datasets that have known success. Finally, CAPRK [15] is a view drone dataset exploited for detecting and counting parked vehicles [1]. [27] presented a large-scale VIRAT dataset with about 550 videos. Download: Download high-res image (322KB) Download: Download To fine-tune and evaluate the proposed sequence classification modalities, a new drone vs bird video classification dataset (DvsB-Vid) has been formed from Drone vs Bird Detection Challenge dataset which consists of a pool of 77 different video sequences containing drones, birds, planes on various backgrounds. Pages 633–644. 07% of The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife This is followed by tracking candidate drone detections for a few frames, cuboid formation, extraction of the 3D convolution feature map, and drones detection within each cuboid. Shapes. From radar and other sensor data, can you detect, classify and track different drones or UAVs. Currently, the major challenge is the development of autonomous operations to complete missions and replace human operators. In this work, we present the first annotated drone dataset from top and back views in badminton doubles and propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance. Fire Detection: The computer vision UAVDT is a large scale challenging UAV Detection and Tracking benchmark (i. Authors claim, that contemporary drones are outfitted with 4K video cameras, and the heightened resolution of the images facilitates modern object detectors in discerning smaller objects. Learn more. Their areal mobility and ability to carry video cameras provide new perspectives in visual surveillance which can impact This work proposed the first approach that can automatically retrieve drone clips from an unlabeled video collection using high‐level search queries, such as “drone clips captured outdoor in daytime from rural places”, and introduced the first large‐scale dataset composed of edited drone videos. The process for building a dataset. They contain trajectories, raw video material, and extensive metadata encompassing 100 variables for each DroneCrowd is a benchmark for object detection, tracking and counting algorithms in drone-captured videos. Abstract. In , the authors propose an anomaly detection technique on the mini-drone video dataset which consists of surveillance videos taken by an UAV. Each drone was setup and operated in a controlled, geofenced environment. Equipped with a large field of view, This dataset contains aerial videos of a closed campus (Manipal Institute of Technology, India) captured using DJI Phantom 3 Professional drone with 1280 x 720 resolution. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. ), and density The MOBDrone benchmark is introduced and publicly release, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination, and a thorough performance analysis of several state-of-the-art object detectors on the MOB Drone data is conducted. Modern Object detection is one of the most common tasks performed by drones in order to track and classify the objects in the drone video. These datasets are video-based action recognition datasets in which a video sequence contains a single action performed by a single human throughout the video. The main reason is that anomalous events occur infrequently and rarely due to the How The Drone Data Set Was Created. OK, Got it. 4GB) (17 videos) OR Download individually: 1. 12 annotated drones. Overall, the video dataset contains 650 videos (365 IR and 285 visible, of ten seconds each), with a total of About. Urban Drone Dataset(UDD) for "Large-scale Structure from Motion with Semantic Constraints of Aerial Images", PRCV2018 Topics. Type of data: Segmented Images, JPEG files for images, Plain Text files for annotations,. Here, we offer a selection of complimentary sample datasets that showcase the potential of drone data. A free flying drone was used to Drone-Action (Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition) For drone videos, most still follow the heuristic based approach [39, 47, 32, 44, 45] and use simulation training from the AirSim [57] platform [37, 36, 52]. To better understand and analyze them, we have created a publicly available video dataset of typical For the task of detecting casualties and persons in search and rescue scenarios in drone images and videos, our database called SARD was built. 3K resolution images UAV-Human is a large dataset for human behavior understanding with UAVs. Drone images and videos were to the action recognition community is aerial videos because drones are an increasingly popular means of capturing videos. Commercial drone platforms capable of detecting basic human actions such as hand gestures have been developed. In total, 300 images have been densely labeled with 8 Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. The proposed dataset consists of 32 high-resolution videos containing 13 action classes with 55,477 frames (without augmentation) and almost 400,000 annotations. 3 watching. , pedestrian, car, and van) from videos taken from drones. The VisDrone2019 dataset is collected by the AISKYEYE team at Lab of Machine Learning and Data Mining, Tianjin University, China. 25 seconds of RF drone communications for 3. Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i. In this context, this dataset of aerial videos UAVDT is a large scale challenging UAV Detection and Tracking benchmark (i. (b) Using a 8K fish-eye video data, we perform video calibration, and player and ball detection using YOLOv5 [21]. Similar to the UCF-ARG dataset, the dataset partition includes 60%, 10%, and 30% of videos for training, validation, and testing Examples of such application-specific drone datasets include datasets for object detection [7,8], datasets for vehicle trajectory estimation [9,10], datasets for object tracking [11,12], datasets for human action recognition [13–16], datasets for gesture recognition [17–19], datasets for face recognition [20,21], a dataset for fault detection in photovoltaic plants [22], datasets for This research presents DroneSURF dataset, a novel large-scale drone videos dataset, collected over two segments, intended to facilitate research for drone-based face detection and recognition. The 29 hours of video in the dataset cover 23 drone video dataset. walking on 1 circle (1 We collected a large-scale dataset, i. xml files in PASCAL-VOC format. ) Traffic at a total of three different roundabouts in Hochiminh city was recorded from a camera-equipped drone. The dataset, sourced from the publicly available "YOLO Drone Detection Dataset" on Kaggle, comprises a diverse set of annotated images captured in The dataset comprises 2,898 infrared thermal images extracted from 43,470 frames in hundreds of videos captured by UAVs in various scenarios, such as schools, parking lots, roads, and playgrounds. However, broadcast Dataset Card for KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos Dataset Summary We present a novel high-quality dataset for animal behavior recognition from drone videos. The proposed algorithms in this paper are trained and evaluated on these benchmark datasets. The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. 4, Fig. MIT [12]. With OpenCV and a custom-trained dataset, it can identify drones via webcam or analyze pre-recorded videos, enhancing security and surveillance. The records are 10. To this end, we introduced the first large-scale dataset composed of edited drone videos. We have built our dataset using scale models of Russian POM-2 and POM-3 surface mines. For each location and surveillance scenario, data is captured twice: (i) during the morning and MOBDrone: A Drone Video Dataset for Man OverBoard Rescue. The "#DS" indicates the number of types of DSs studied. Thus, there is an intense demand for trajectory datasets of traffic participants (TPs) in intersections. Most of the datasets are confined to To train our approach, we needed numerous examples of edited drone videos. drones. The Stanford Drone Dataset comprises of more than 100 different top-view scenes for a total of 20,000 targets engaged in various types of interactions. 07% of scene category, quality, and shot type, the dataset is a sample of edited drone videos, captured and edited by human. MIT license Activity. Perera 1,* , Yee Wei Law 1 and Javaan Chahl 1,2 1 School of Engineering, University of South Australia, A number of video datasets have been published for general human action recognition and aerial human action recognition. The created dataset consists of 38 different contents captured in full HD resolution, with a duration of 16 to 24 seconds each, shot with the mini-drone Phantom 2 Vision+ in a parking lot. The dataset contents can be clustered in Load Visdrone Dataset in Python fast. , object DETection (DET), Single Object Tracking (SOT) and Multiple Object Tracking (MOT). , 50–150 m) and angles (45 and 90 degrees), making it a valuable Drone-Action (Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition) To facilitate the development and evaluation of drone detection models, we introduce a novel and comprehensive dataset specifically curated for training and testing drone detection algorithms. Domain randomization is used to vary the simulation parameters such boxes. Therefore, even though the speed of dispersal of such landmines is much easier than buried mines, drone datasets can help with their detection. Our work represents an initial stride toward addressing these needs. I obtained it as our research collaborater had obtained prior permission. This dataset facilitates thorough evaluation and The dataset for this study was meticulously gathered using a drone (Dronematrix YACOB and DJI Mavic2) equipped with high-resolution cameras (4000*2250 and 3840*2160 pixels). perera@mymail. This dataset Experimental results based on drone video datasets demonstrate that our approach improves detection accuracy in the case of small-scale instances and reduces false positive detections. The Stanford Drone Dataset Download the estimated joint annotations obtained using OpenPose and the bounding boxes. To this end, Artificial Intelligence techniques can be leveraged for the automatic The dataset benchmarks are necessary when evaluating previous methods with a uniform standard, which is shown in TABLE I. Previous datasets included mostly single Dataset containing IR, visible and audio data that can be used to train and evaluate drone detection sensors and systems. Please send an email to asanka. A tiling method was subsequently applied to create a pyramid of images at various scales from the original 5. Therefore, for training, we use only frames that provide the annota-tions, and for Drone Action [17] is an outdoor drone video dataset providing 240 HD video clips recorded from low altitudes and at low speeds across 13 dynamic human actions. Each action contains 50 videos. au to get the video dataset Dataset containing IR, visible and audio data that can be used to train and evaluate drone detection sensors and systems. Overall, the video dataset contains 650 videos (365 IR and 285 visible, of ten seconds each), with a total of drone video dataset that consists of surveillance videos. Instead, the motivation was to resemble a variety of edited drone videos. The test set is composed of 120 images, including images extracted by video acquired by the drone and a portion of images from Kaggle dataset. ICD Drone Dataset [] is designed to assist 3D reconstruction of single buildings, so the drones fly low. Drones became popular video capturing tools. 3K resolution images Aeroscapes [] dataset was inspired by Cityscapes and assumes that drones can provide more information than cameras on cars. Watchers. This dataset provides 37 training video sequences and 22 testing video sequences from 7 different realistic scenes with various anomalous events. 5 h, captured across 206K frames with ten abnormal event types. These include data from one 4-way and two 3-way intersections, and more than 800 minutes of video per data set. Overall, the video dataset contains 650 videos (365 IR and 285 visible, of ten seconds each), with a total of The dataset contains a total of 123 video sequences and more than 110K frames making it the second largest object tracking dataset after ALOV300++. e. The dataset, curated from videos taken of Kenyan wildlife, currently contains behaviors of giraffes, plains zebras, and Grevy’s zebras, and will soon be expanded to other species, including baboons. , man overboard (MOB). Download all (3. The resulting dataset contains 51 videos with total data traffic of nearly 6. 3K All videos in the dataset are in HD format (1920x1080). The The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an emerging application attracting significant attention from researchers in various areas of computer vision. If you go over any of these In order to better understand human interactions, drone videos are taken from 8 scenes in various locations such as the university campus, sidewalks, etc. The neural network is trained on synthetic images and tested on a real-life dataset of drone videos. By introducing a novel dataset collected from drone videos in the natural habitats of Kenyan wildlife, we aim to enrich the current pool of resources available for the study of animal behavior. A free flying drone was used to record 13 dynamic To fill this gap and enable research in wider application areas, we present an action recognition dataset recorded in an outdoor setting. This report is the first of an attempt to. mixed 39. With the increasing use of drones for surveillance and monitoring purposes, there is a growing need for reliable and efficient object detection algorithms that can detect and track objects in aerial images and videos. We add evaluation section (Tools for trajectory and SLAM methods evaluation) We add a new UAV dataset, UZH-FPV Drone Racing Dataset, which aims high speed state estimation using RGB, Event, and IMU. Data has been captured at two outdoor locations: (i) at the ground level, where subjects are asked to walk in a park-like environment, and (ii) at the terrace of a building. The dataset, sourced from the publicly available "YOLO Drone Detection Dataset" on Kaggle, comprises a diverse set of annotated images captured in VisDrone Dataset. classification 38. , (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi Stanford researchers have created the first large-scale dataset of aerial videos from multiple classes of targets interacting in complex outdoor spaces. Download the estimated joint annotations obtained using OpenPose and the bounding boxes. Employing such a self-supervised methodology, we create the first, to the best of our knowledge, indoor flight dataset annotated with real distance labels to the closest obstacle towards three diverging directions in the field of view of the UCF ARG [11], UCF-aerial action [12] datasets are created by a camera mounted on a helium balloon and R/C controlled blimp, simulating the behavior of a drone. Here are our top picks for the Best Drone Video Datasets out there: 1. Mentioning: 27 - Aerial human action recognition is an emerging topic in drone applications. Experimental results based on drone video datasets demonstrate that our approach improves detection accuracy in the case of small-scale instances and reduces false positive detections. , DJI Mavic, Phantom series 3, and 3A, in various scenarios, To minimise human effort, self-supervised methods can be adopted to automate the collection and annotation process of large scale datasets. Nevertheless, it is still a challenge when the objects are hard to distinguish, especially in low light conditions. categories (e. The VisDrone2019 Dataset. Despite this capability, many drone image datasets The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. The CVUSA [] defined a ground-satellite matching task, which provides a dataset involving street-view images and satellite-view images captured from distinct regions across the United States. The dataset was collected by a flying UAV in multiple VisDrone2019-VID dataset. Aerial Maritime Drone Dataset-> bounding boxes; RetinaNet for pedestrian detection-> bounding boxes; BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos-> Thermal IR videos of humans and animals MMSPG Mini-drone video dataset MMSPG Scalable Video Database Mobile 3DTV content delivery optimization over DVB-H system Motion Blur Datasets This dataset contains 403 haze videos captured in real outdoor scenes Youku-V1K The resulting dataset contains 51 videos with total data traffic of nearly 6. Few works have been done to date on creating datasets of images taken by drones of people in marine environments. of flight videos, drone datasets [11], [22], [23] do not. , about 80, 000 representative frames from 10 hours raw videos) for 3 important fundamental tasks, i. The three drone types of the dataset. In order to publish our ReID dataset to the public, we have to obtain the consent forms from all the captured subjects, and then we are allowed to distribute the videos containing the captured subjects to the community. 30 videos, 60 Genius Mode messages, 60 Genius Mode images, and 5 Genius Mode videos per month. , 1935), was the analysis of traffic flow and the development and validation of traffic flow models. counting 4. Applications and challenges in video surveillance via drone: A brief survey. walking on 1 circle (1 The dataset contains 200 videos of 58 subjects, captured across 411K frames, having over 786K face annotations. Unmanned Aerial Vehicle (Rotary Wing Unmanned Aerial Vehicles) The dataset can be used to develop new algorithms for drone detection using multi-sensor fusion from infrared and visible videos and audio files. 288 video clips composed of 261,908 frames and 10,209 static photos. The dataset covers a specific range of heights (i. Drone-based optical-thermal blade video data acquisition when the wind turbine is in normal operation. Significant effort has been focused on building larger broadcast sports video datasets [8, 14]. The proposed model is built in two modules. mask Mini-drones are increasingly used in video surveillance. Building highly complex autonomous UAV systems that aid in SAR The foundation of our dataset is videos from drones flying over the Mpala Research Centre in Kenya. Scenes in it are relatively simple. Stars. Captured from satellites, planes, and drones, these projects can help you find objects of interest in overhead photos. Overall, a total of seven-hour long videos from 15 days in July and August 2021 were captured. VisDrone is a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, to make vision meet drones. Thereafter, for both data, we perform player The dataset provides segmented image files of drones and birds for computer vision and pattern recognition applications. Tasks. Vehicle trajectory data have become essential for many research fields, such as traffic flow, traffic safety and automated driving. However, a limited number of aerial video datasets are available to support increased research into aerial human action analysis. To this end, Artificial Help us build a catalog of datasets for testing OpenDroneMap. polygon 29. Provide a path to the folder containing the videos and where to output the motion boundaries before running the code file. While We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. Attempts were then made to acquire and image the data storage areas on each drone, the controller, connected mobile devices and computers. The major challenges are: First, small-sized object (drone): in the cap-tured aerial videos, typical target drones are only 0. edu. Results that are generated from models using private datasets, and results without such details will be excluded from prize evaluation. The dataset was collected by a flying UAV in multiple The benchmark dataset consists of 288 video clips composed of 261,908 frames and 10,209 static photos collected by several drone-mounted cameras, encompassing a wide variety of features such as location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, automobiles, bicycles, etc. For their Drone Forensics Program, VTO purchased 79 drones: 30 drone models, ~3 of each model. Similar to the UCF-ARG dataset, the dataset partition includes 60%, 10%, and 30% of videos for training, validation, and testing respectively. Unmanned Aerial Vehicles (UAVs) or drones are often used to reach remote areas or regions which are inaccessible to humans. The KABR dataset contains annotated video behavior of zebras and giraffes at the Mpala Research Centre. Oh et al. . We provide a four-class fine annotation (Greenery, Construction, Road, and Water Bodies) describing the general layout of the scene. There's no test section of this We are pretty sure that there will be many 'new' and 'hot' datasets intruduced at the workshop. The objects of interest in this benchmark A43: Vehicle Trajectory Dataset from Drone Videos Including Off-Ramp and Congested Traffic. In the NTUT 4K Drone Photo Dataset for Human Detection authors furnish 4K photos extracted from drone videos captured in Taiwan. Therefore, for training, we use only frames that provide the annota-tions, and for The authors have made available the VisDrone2019-DET Dataset, a comprehensive collection of drone-captured images tailored for object detection tasks. 1. Videos Upcoming . we use not only our Drone-Anomaly dataset but also another dataset. Report repository The drone-vs-birds challenge aims at meauring and driving progress in the automated detection of small drones in image data. Medium datasets have fewer than 500 images. The obtained dataset comprises 1,981 manually labeled images extracted from video frames. This dataset contains several agents such as pedestrians, bicyclists, skateboarders, cars, buses, and golf carts. unisa. However, the scenes captured in Aeroscapes are often repetitive and lack satisfactory resolution. Two new datasets for SAR with drones have recently been released that Drone Action [17] is an outdoor drone video dataset providing 240 HD video clips recorded from low altitudes and at low speeds across 13 dynamic human actions. Treiterer (1975) was among the first researchers to collect vehicle Researchers have made mount of efforts in this area and achieved considerable progress. Their areal mobility and ability to carry video cameras provide new perspectives in visual surveillance which can impact privacy in ways that have not been considered in a typical surveillance scenario. Introduction From a general perspective, an anomaly is a pattern that differs from a standard pattern. , 2022). 6). The dataset contains 90 audio clips and 650 videos (365 IR and 285 visible). xlsx file for reference link of videos and YOLOv7 PyTorch: How the data were acquired? The dataset consists of 42 different bird and This dataset contains the drone telemetry data associated with the KABR dataset. VisDrone2019-VID dataset. Previous Chapter Next Chapter. Here, we briefly discuss some The Annotation files of Stanford Drone Dataset. , (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi This paper proposes SoccerTrack, a dataset set consisting of GNSS and bounding box tracking data annotated on video captured with a 8K-resolution fish-eye camera and a 4K-resolution drone camera. Some videos of this dataset were used for the experiments in our "Dymanic Classifer Selection" paper. Videos were recorded while the drone was moving towards and away from the subject. Labelimg was used to label the ship targets in the The benchmark dataset consists of 288 video clips composed of 261,908 frames and 10,209 static photos collected by several drone-mounted cameras, encompassing a wide variety of features such as location (taken from 14 KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos - dirtmaxim/kabr Computer Vision Analysis: The video stream or images captured by the drone's camera are sent to a computer vision model for analysis. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. SeaDronesSee is a large-scale data set aimed at helping develop systems for Search and Rescue (SAR) using Unmanned Aerial Vehicles (UAVs) in maritime scenarios. The drone flights, conducted from 09/2021 to 09/2023, captured a wide array of images under different environmental conditions. rtk, large. By exploiting the temporal information and aggregating the feature maps, our two-stream method improves the detection performance by 8. It contains carefully annotated ground truth data for various computer vision tasks related to drone-based image and video analysis. dataset semantic-segmentation drone-dataset Resources. The benchmark dataset consists of 288 video clips formed by 261,908 frames and 10,209 static Vehicle trajectory data or microscopic traffic data are highly valuable for a wide range of applications. Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition: Asanka G Perera, Yee Wei Law, Javaan Chahl: Download. In the VisDrone-SOT2020 Challenge, we collect 167 drone videos with more than 189K frames. The VisDrone Dataset is a large-scale benchmark created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. The baboon detection dataset was created by manually annotating all baboons in drone videos with bounding boxes. The videos in this dataset contain large and fast camera motion and aerial videos are captured at variable heights. Spanning various urban and suburban locales across 14 different cities in China, from north to south, the VisDrone dataset stands as the most extensive of its kind ever published. Per-video MATLAB files and visualization code: Split sets: Paper: Our MOBDrone Dataset, which we publicly released at , aims to overcome the lack of large public datasets of drone-based imagery for overboard human detection. If you would like to contribute a dataset, please post in the forum. Its realization required nearly 80 hours of work between data acquisition, post-processing, and annotation, involving, among others, a certified pilot of the Fly&Sense Service of the CNR of Pisa for UAV To facilitate the development and evaluation of drone detection models, we introduce a novel and comprehensive dataset specifically curated for training and testing drone detection algorithms. Video labels: Airplane, Bird, Drone and Helicopter. UAV-Human [20] is a large benchmark The foundation of our dataset is videos from drones flying over the Mpala Research Centre in Kenya. To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. Large datasets have more than 500 images. 25 seconds of RF background activities and approximately 5. To develop and test such algorithms, datasets of aerial videos captured from drones are essential. Whether you're a seasoned GIS professional, a budding enthusiast, or simply curious about the impact of drone technology, these samples provide a valuable opportunity to dive into the world of high-resolution imagery, LiDAR data, thermal scans UAVDT is a large scale challenging UAV Detection and Tracking benchmark (i. Drone Surveillance of Faces, is a large-scale drone dataset intended to facilitate research for face recognition using drones. (b) DJI Phantom 4 Pro. In this paper, we create a new dataset, named DroneAnomaly, for anomaly detection in aerial videos. Share on. It serves as a widely adopted benchmark dataset for video object detection in the drone domain. Therefore, anomalies depend on the phenomenon of interest. The proposed dataset demonstrates variations MRP Drone dataset (2014) [17] Person re-identification Yes ˘16,000 frames No MiniDrone dataset (2015) [6] Area monitoring Yes 22,860 frames No Request PDF | On Jun 1, 2022, Atom Scott and others published SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos | Find, read and cite all the research you Since existing crowd counting datasets merely focus on crowd counting in static cameras rather than density map estimation, counting and tracking in crowds on drones, we have collected a new large-scale drone-based dataset, DroneCrowd, formed by 112 video clips with 33,600 high resolution frames (i. the MDV dataset. Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. BioDrone highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. Following VisDrone-SOT2019 [], the VisDrone-SOT2020 dataset is divided into three subsets, including training set containing 86 videos with 70K frames, validation set containing 11 videos with 7K frames and testing set containing 95 videos with 145K frames. Drone Video Dataset for Man OverBoard Rescue}, author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and This study presents a novel dataset from drone videos for baboon detection, tracking, and behavior recognition. The videos were collected by flying drones over animals at For fairness, teams need to specify what public datasets are used for training/pre-training their models in their challenge_ report. Authors claim, that contemporary drones are Download Citation | A Drone Video Clip Dataset and its Applications in Automated Cinematography | Drones became popular video capturing tools. , (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi We present a convolutional neural network (CNN) that identifies drone models in real-life videos. We publicly release this dataset in the Zenodo repository [Zenodo Link]. point 3. The UAV-Human dataset contains 155 action classes, and the action categories are listed below: ERA-> A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos. MOBDrone: A Drone Video Dataset for Man OverBoard Rescue. Captured from satellites, planes, and drones, these projects can help you find objects of interest in This dataset contains the drone telemetry data associated with the KABR dataset. About; Suggest dataset . This dataset is also used for training and validating our drone video filtering algorithm. It contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. Our dataset is specifically built for surface landmine object class. VIGOR [] redefined the problem by breaking the VisDrone is a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, to make vision meet drones. Drone Video Dataseet. 48% in mean Average UAV-Human is a large dataset for human behavior understanding with UAVs. Alternatively, if you want to create your own dataset, follow these steps: Collect images from Kaggle Dataset or Google Images. On average, the video sequences consist of 1,384 frames, while each frame contains 1. The dataset was collected by a flying UAV in multiple BioDrone is the first bionic drone-based single object tracking benchmark, it features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. In the first phase, the object detection model is developed using YOLOv5 which detects and classifies military vehicles such as Military Tanks and Armoured Personnel Carriers (APCs). Large-scale and high-quality benchmark Each video in the dataset contains approximately 40–50 subjects for the two crowds, with 15–30 subjects per combination of CD, angle of view, drone SS (static or dynamic), and time of capture. Action Classes. Drone-to-Drone detection has a more challenging nature compared to standard object detection problems. For This paper proposes SoccerTrack, a dataset set consisting of GNSS and bounding box tracking data annotated on video captured with a 8K- resolution fish-eye camera and a 4K-resolution drone camera, which is expected to provide a more robust foundation for designing MOT algorithms that are less reliant on visual cues and more reliant on motion analysis. MOBDrone: A Drone Video Dataset for Man OverBoard Rescue; Article. At latest, the dataset has grown to 1,13,766 images which includes annotated images with 6 object classes out of which only human class is required, that consists of 50,000 images used for The dataset for this study was meticulously gathered using a drone (Dronematrix YACOB and DJI Mavic2) equipped with high-resolution cameras (4000*2250 and 3840*2160 pixels). cell-wise segmentation 1. A drone video clip DroneDeploy is a leading provider of cloud-based software for drone mapping, offering a comprehensive platform for drone flight planning, data collection, and analysis. – Multi-object tracking aims to recover the object trajectories in video sequences. This is followed by tracking candidate drone detections for a few frames, cuboid formation, extraction of the 3D convolution feature map, and drones detection within each cuboid. The dataset might not be representative of all edited drone videos, as the distribution of latter is highly diverse and not known exactly. The dataset is updated constantly. 9 forks. bounding box 208. Stream Visdrone while training ML models. To increase the robustness of the SARD data, an extension of the SARD set, called Corr, was created that Recently, the Electronics and Telecommunications Research Institute (ETRI) released a new drone dataset called the DNA+Drone Dataset , which includes 4K high-resolution images and videos captured by drones in outdoor environments. This telemetry dataset contains information about the status drone during the missions, including location and altitude, along with the bounding box dimensions Intersection is one of the most challenging scenarios for autonomous driving tasks. The benchmark dataset consists of 288 video clips formed by 261,908 frames and 10,209 static @inproceedings{scott2022soccertrack, title={SoccerTrack: A Dataset and Tracking Algorithm for Soccer With Fish-Eye and Drone Videos}, author={Scott, Atom and Uchida, Ikuma and Onishi, Masaki and Kameda, Yoshinari and Fukui, Kazuhiro and Fujii, Keisuke}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3569- For the Drone-vs-Bird Detection Challenge 2021, 77 different video sequences have been made available as training data. g. The proposed anomaly detector is a combination of a Convolutional Neural Network (VGG16) and a Recurrent Neural Network (LSTM), trained using supervised learning. KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos - dirtmaxim/kabr We have tested our method on very complex datasets Mini-drone video Bonetto et al. Drone videos in the wild are first captured and then Urban Drone Dataset(UDD) for "Large-scale Structure from Motion with Semantic Constraints of Aerial Images", PRCV2018 Topics. A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos Atom Scott*, Ikuma Uchida*, Masaki Onishi, Yoshinari Kameda, Kazuhiro Fukui, Keisuke Fujii Presented at CVPR Workshop on Computer Vision for Sports (CVSports'22). Combining UAV imagery with study of dynamic salience further extends the number of future applications. , 1920x1080) captured in 70 different scenarios. Both quantitative and qualitative evaluations have confirmed the validity of our method. The Man OverBoard Drone (MOBDrone) dataset is a large-scale collection of aerial footage images. Indeed, considerations of In drone applications that require high-speed processing, a single-stage, lightweight network like YOLO V5 is advantageous. (Results using private datasets can still be included in the report. 48% in mean Average The MOBDrone benchmark is a large-scale drone-view dataset suitable for detecting persons overboard. In the splitting operation, the high-resolution images of the public dataset were used in the training dataset, more information can be extracted in the training phase . In addition to a benchmark tracking algorithm, we include code for camera calibration and other preprocessing. To tackle this problem, we construct a large-scale drone-based RGB-Infrared vehicle detection dataset, termed DroneVehicle. The VisDrone2019 dataset is collected by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin A new challenging winter dataset with 6 cameras, snow covered background and multiple drones. proposed using a fast object detection model like YOLO to detect people in drone videos. 5 hours In , the authors propose an anomaly detection technique on the mini-drone video dataset which consists of surveillance videos taken by an UAV. Download: Download high-res image (322KB) Download: Download We present a novel in-situ dataset for animal behavior recognition from drone videos. We collected a large-scale dataset, i. The dataset consists of recorded segments of RF background activities with no drones, and segments of drones operating in different modes such as: off, on and connected, hovering, flying, and video recording (see Fig. Furthermore, the development of multi-object detection and tracking algorithms is supported by datasets like VisDrone and UAVDT. Although drone detection from aerial videos has crucial prospects, it is an under-explored research problem. , behavior modeling, motion prediction, safety validation, etc. The training and evaluation of models are conducted using 51 video snippets from the training set and 7 snippets from the validation set, respectively. In total, 8 different types of drones exist in the dataset , i. Follow the links below to the download the datasets. Contribute to flclain/StanfordDroneDataset development by creating an account on GitHub. Dataset Ninja. boxes. The annotations are provided as text files with one line From radar and other sensor data, can you detect, classify and track different drones or UAVs. The proposed dataset demonstrates variations MRP Drone dataset (2014) [17] Person re-identification Yes ˘16,000 frames No MiniDrone dataset (2015) [6] Area monitoring Yes 22,860 frames No Download fl-drones dataset annotations as described above. mask 145. su Traffic at a total of three different roundabouts in Hochiminh city was recorded from a camera-equipped drone. A few examples of videos in this dataset are shown below. (a) Hubsan H107D + . For each video half the frames are in training & rest are in validation or testing. The dataset includes two subsets: 25 videos with To fill this gap and enable research in wider application areas, we present an action recognition dataset recorded in an outdoor setting. 5, Fig. Unmanned Aerial Vehicles (UAVs) or drones are often used to reach remote areas or regions which are The dataset contains 200 videos of 58 subjects, captured across 411K frames, having over 786K face annotations. The UAV dataset consists of 30 video sequences capturing 4K high-resolution images in slanted views. To create the training and validation datasets, we show a method of generating synthetic drone images. 84 stars. This telemetry dataset contains information about the status drone during the missions, including location and altitude, along with the bounding box dimensions Kashihara et al. The ground truth drone trajectory is estimated by fusing total station tracking and onboard IMU data. Drone Video Dataset for Man OverBoard Rescue}, author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. In order to make trajectory data usable for researchers, an overview of the included road section and traffic VisDrone2019-VID dataset. The dataset is captured by UAVs in various complex scenarios. The dataset contents can be clustered in three categories: normal, suspicious, and illicit behaviors. In this paper, based on the type of The MOBDrone benchmark is a large-scale drone-view dataset suitable for detecting persons overboard. Download: Download high-res image (322KB) Download: Download All videos in the dataset are in HD format (1920x1080). The proposed approach is evaluated on two publicly available drone detection datasets and outperforms several competitive baselines. ihnom yedjrgwc fnaerh elo vbldrce borxqh sraplju yrj fsbrqr zwfeam