Vision-Based Unmanned Aerial Vehicle Detection And Tracking For Sense And Avoid Systems
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(2016)
摘要
We propose an approach for on-line detection of small Unmanned Aerial Vehicles (UAVs) and estimation of their relative positions and velocities in the 3D environment from a single moving camera in the context of sense and avoid systems. This problem is challenging both from a detection point of view, as there are no markers on the targets available, and from a tracking perspective, due to misdetection and false positives. Furthermore, the methods need to be computationally light, despite the complexity of computer vision algorithms, to be used on UAVs with limited payload.To address these issues we propose a multi-staged framework that incorporates fast object detection using an AdaBoost-based approach, coupled with an on-line visual-based tracking algorithm and a recent sensor fusion and state estimation method. Our framework allows for achieving real-time performance with accurate object detection and tracking without any need of markers and customized, high-performing hardware resources.
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关键词
vision-based unmanned aerial vehicle detection,vision-based unmanned aerial vehicle tracking,sense-and-avoid system,relative position estimation,relative velocity estimation,3D environment,UAV,multistaged framework,object detection,AdaBoost-based approach,online visual-based tracking algorithm,sensor fusion,state estimation method
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