Detection, Localization, and Tracking of Multiple MAVs With Panoramic Stereo Camera Networks

Malicious use of micro aerial vehicles (MAVs) has become a serious threat to public safety and personal privacy in recent years. Motivated by this problem, we propose a systematic approach to monitor the intrusion of malicious MAVs based on a novel type of panoramic stereo camera networks. Each sensing node of such a network consists of 16 lenses that can form a 360-degree panoramic vision system. The 16 lenses further form 8 pairs of stereo cameras that can directly localize aerial targets. The effective range for a sensing node localizing a MAV like DJI M300 could reach 80 meters, which is much farther than existing commercial stereo cameras. In terms of algorithms, we propose i) a novel visual MAV detection algorithm based primarily on motion features of MAVs, ii) an efficient stereo localization algorithm based on sparse feature points, and iii) robust multi-target tracking and trajectory fusion algorithm to fuse the observations of different sensing nodes. The effectiveness, robustness, and accuracy of the proposed algorithms together with the overall system have been verified by extensive experimental tests. To the best of our knowledge, this is the first systematic approach to detect, localize, and track unknown MAVs in the literature. Our approach provides a scalable solution to securely cover large areas of interest against malicious MAV intrusion.