The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline

With the increasing popularity of Unmanned Aerial Vehicles (UAVs) in computer vision-related applications, intelligent UAV video analysis has recently attracted the attention of an increasing number of researchers. To facilitate research in the UAV field, this paper presents a UAV dataset with 100 v...

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Bibliographic Details
Published in:International journal of computer vision 2020-05, Vol.128 (5), p.1141-1159
Main Authors: Yu, Hongyang, Li, Guorong, Zhang, Weigang, Huang, Qingming, Du, Dawei, Tian, Qi, Sebe, Nicu
Format: Article
Language:English
Subjects:
Artificial Intelligence
Benchmarks
Computer Imaging
Computer Science
Computer vision
Datasets
Drone aircraft
Image Processing and Computer Vision
Machine vision
Multiple target tracking
Object recognition
Pattern Recognition
Pattern Recognition and Graphics
Unmanned aerial vehicles
Vehicles
Vision
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Summary:With the increasing popularity of Unmanned Aerial Vehicles (UAVs) in computer vision-related applications, intelligent UAV video analysis has recently attracted the attention of an increasing number of researchers. To facilitate research in the UAV field, this paper presents a UAV dataset with 100 videos featuring approximately 2700 vehicles recorded under unconstrained conditions and 840k manually annotated bounding boxes. These UAV videos were recorded in complex real-world scenarios and pose significant new challenges, such as complex scenes, high density, small objects, and large camera motion, to the existing object detection and tracking methods. These challenges have encouraged us to define a benchmark for three fundamental computer vision tasks, namely, object detection, single object tracking (SOT) and multiple object tracking (MOT), on our UAV dataset. Specifically, our UAV benchmark facilitates evaluation and detailed analysis of state-of-the-art detection and tracking methods on the proposed UAV dataset. Furthermore, we propose a novel approach based on the so-called Context-aware Multi-task Siamese Network (CMSN) model that explores new cues in UAV videos by judging the consistency degree between objects and contexts and that can be used for SOT and MOT. The experimental results demonstrate that our model could make tracking results more robust in both SOT and MOT, showing that the current tracking and detection methods have limitations in dealing with the proposed UAV benchmark and that further research is indeed needed.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-019-01266-1