关键词: 深度学习, 行人检测, 行人匹配, 时空约束

Abstract:

Because most of the previous pedestrian tracking methods cannot effectively solve the problem of target occlusion and target size change and re-tracking, the deep learning method is introduced, but it is found through experiments that the use of deep learning pedestrian tracking will be due to the error of pedestrian detection. And the overall tracking accuracy is not high. A pedestrian tracking method based on deep learning and spatiotemporal constraint post-processing is proposed. The pedestrian detection part of deep learning adopts SSD algorithm. The pedestrian matching part adopts a method of calculating cross-input field difference and then performing block summarization, finally, the post-processing of space-time constraints is carried out. Experiments on the OTB dataset are compared with the traditional tracking algorithm and the pure deep learning algorithm.

Key words: deep learning, pedestrian detection, pedestrian matching, space-time constrain