[2D1] [General Session] 13. AI Application

[2D1-01] Parallel Implementation of Probability Occupancy Map Generation for Multiple Human Tracking Based on Graph Optimization

〇Yuri Nishikawa^1,2, Hitoshi Sato¹, Jun Ozawa¹ (1. National Institute of Advanced Industrial Science and Technology (AIST), 2. Panasonic Corporation)

Keywords:Multiple Human Tracking, Probability Occupancy Map, Parallel Processing

Multi-camera multiple object tracking methodology that uses probability occupancy map (POM) and graph optimization can efficiently track multiple people in spite of significant occlusion, even without appearance model and prerequisite knowledge about the number of people in the target area. However, the computation time of POM increases according to grid size and number of cameras, which limited latency and throughput. In this paper, we report the effect of hierarchically applying two parallelization techniques to enhance the performance of POM generation. As a result, we achieved more than 20 times higher throughput compared to sequential processing, and reduce latency by 66%.