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Vision-Based Multiobject Tracking Through UAV Swarm
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AI
DOI：10.1109/LGRS.2023.3305675.png)
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Abstract
En 中文
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.
Keywords:
Index Terms-Multiobject tracking
spatial and temporal assignment
unmanned aerial vehicle (UAV) swarm
vision-based tracking
Journal
IF:
16.4
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1.0W
Citations:
5.1K