Vision-Based Multiobject Tracking Through UAV Swarm

Conventional multisensor multiobject tracking algorithms usually fuse local positions to construct the global situation. However, low-cost cameras that have been widely used in small-scale unmanned aerial vehicles (UAVs) only provide bearing angle measurement but not target positions, which prohibits the application of conventional tracking paradigms. We propose a solution of vision-based multiobject tracking through UAV swarm. Given the videos captured by UAVs and the states of the UAVs, the proposed solution fuses visual and geometry information to tackle three tasks: 1) associating the targets reported by different UAVs; 2) computing the targets' positions in inertial coordinate system; and 3) associating the targets reported at different instants. The effectiveness of the proposed solution is evaluated by offline ablation experiments, field scene experiments, and online closed-loop simulation.