Vision-Based Multiobject Tracking Through UAV Swarm
Hao Shen, Defu Lin, Xiwen Yang, Shaoming He
Abstract
Conventional multi-sensor multi-object tracking algorithms usually fuse local positions to construct the global situation. However, low-cost cameras that have been widely-used in small-scale 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 multi-object 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. (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. The source code is available at https://github.com/bitshenwenxiao/MOTL and the simulation video is available at https://youtu.be/erCiENAOEaM.