Deep learning architecture for pedestrian 3-D localization and tracking using multiple cameras

Kikyung Kim, Byeongho Heo, Moonsub Byeon, Jin Young Choi

Abstract: In this paper, we propose a novel deep-learning architecture for accurate 3-D localization and tracking of a pedestrian using multiple cameras. The deep-learning network is composed of two networks: detection network and localization network. The detection network yields the pedestrian detections and the localization network estimates the ground position of a pedestrian within its detection box. In addition, an attentional pass filter is introduced to effectively connect the two networks. Using the detection proposals and their 2-D grounding positions obtained from the two networks, multi-camera multi-target 3-D localization and tracking algorithm is developed through min-cost network flow approach. In the experiments, it is shown that the proposed method improves the performance of 3-D localization and tracking.