Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the evelopments of object detection and tracking algorithms, we have organized two challenge workshops in conjunction with ECCV 2018, and ICCV 2019, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first presents a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms on the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from the website: this https URL.

Vision Meets Drones: Past, Present and Future

Deep learning-based object detection in low-altitude UAV datasets: A survey

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/VisDrone/VisDrone-Dataset</uri> . 

/pdf/detection-and-tracking-meet-drones-challenge-1zulpzpi.pdf

Detection and Tracking Meet Drones Challenge

Due to a growing number of people who carry out various adrenaline activities or adventure tourism and stay in the mountains and other inaccessible places, there is an increasing need to organize a search and rescue operation (SAR) to provide assistance and health care to the injured. The goal of SAR operation is to search the largest area of the territory in the shortest time possible and find a lost or injured person. Today, drones (UAVs or drones) are increasingly involved in search operations, as they can capture a large, controlled area in a short amount of time. However, a detailed examination of a large amount of recorded material remains a problem. Even for an expert, it is not easy to find searched people who are relatively small considering the area where they are, often sheltered by vegetation or merged with the ground and in unusual positions due to falls, injuries, or exhaustion. Therefore, the automatic detection of persons and objects in images/videos taken by drones in these operations is very significant. In this paper, the reliability of existing state-of-the-art detectors such as Faster R-CNN, YOLOv4, RetinaNet, and Cascade R-CNN on a VisDrone benchmark and custom-made dataset SARD build to simulate rescue scenes was investigated. After training the models on selected datasets, detection results were compared. Because of the high speed and accuracy and the small number of false detections, the YOLOv4 detector was chosen for further examination. YOLOv4 model results related to different network sizes, different detection accuracies, and transfer learning settings were analyzed. The model robustness to weather conditions and motion blur were also investigated. The paper proposes a model that can be used in SAR operations because of the excellent results in detecting people in search and rescue scenarios.

/pdf/automatic-person-detection-in-search-and-rescue-operations-55nr5l6i18.pdf

Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors

Directly benefiting from the deep learning methods, object detection has witnessed a great performance boost in recent years. However, drone-view object detection remains challenging for two main reasons: (1) Objects of tiny-scale with more blurs w.r.t. ground-view objects offer less valuable information towards accurate and robust detection; (2) The unevenly distributed objects make the detection inefficient, especially for regions occupied by crowded objects. Confronting such challenges, we propose an end-to-end global-local self-adaptive network (GLSAN) in this paper. The key components in our GLSAN include a global-local detection network (GLDN), a simple yet efficient self-adaptive region selecting algorithm (SARSA), and a local super-resolution network (LSRN). We integrate a global-local fusion strategy into a progressive scale-varying network to perform more precise detection, where the local fine detector can adaptively refine the target’s bounding boxes detected by the global coarse detector via cropping the original images for higher-resolution detection. The SARSA can dynamically crop the crowded regions in the input images, which is unsupervised and can be easily plugged into the networks. Additionally, we train the LSRN to enlarge the cropped images, providing more detailed information for finer-scale feature extraction, helping the detector distinguish foreground and background more easily. The SARSA and LSRN also contribute to data augmentation towards network training, which makes the detector more robust. Extensive experiments and comprehensive evaluations on the VisDrone2019-DET benchmark dataset and UAVDT dataset demonstrate the effectiveness and adaptivity of our method. Towards an industrial application, our network is also applied to a DroneBolts dataset with proven advantages. Our source codes have been available at https://github.com/dengsutao/glsan .

A Global-Local Self-Adaptive Network for Drone-View Object Detection

Recently, automatic visual data understanding from drone platforms becomes highly demanding. To facilitate the study, the Vision Meets Drone Object Detection in Image Challenge is held the second time in conjunction with the 17-th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones. Results of 33 object detection algorithms are presented. For each participating detector, a short description is provided in the appendix. Our goal is to advance the state-of-the-art detection algorithms and provide a comprehensive evaluation platform for them. The evaluation protocol of the VisDrone-DET2019 Challenge and the comparison results of all the submitted detectors on the released dataset are publicly available at the website: http: //www.aiskyeye.com/. The results demonstrate that there still remains a large room for improvement for object detection algorithms on drones.

/pdf/visdrone-det2019-the-vision-meets-drone-object-detection-in-18qckinuew.pdf

Hao Qi

Papers

VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results