Optimizing Camera Configurations for Multi-View Pedestrian Detection
Yunzhong Hou Xingjian Leng Tom Gedeon Liang Zheng
Jointly considering multiple camera views (multi-view) is very effective for
pedestrian detection under occlusion. For such multi-view systems, it is
critical to have well-designed camera configurations, including camera
locations, directions, and fields-of-view (FoVs). Usually, these configurations
are crafted based on human experience or heuristics. In this work, we present a
novel solution that features a transformer-based camera configuration
generator. Using reinforcement learning, this generator autonomously explores
vast combinations within the action space and searches for configurations that
give the highest detection accuracy according to the training dataset. The
generator learns advanced techniques like maximizing coverage, minimizing
occlusion, and promoting collaboration. Across multiple simulation scenarios,
the configurations generated by our transformer-based model consistently
outperform random search, heuristic-based methods, and configurations designed
by human experts, shedding light on future camera layout optimization.