Deep Learning Technique Based Surveillance Video Analysis for the Store

AI technology has developed so fast, and it has been applied to the commercial area. In order to predict the customer preference and adjust the placement of product or advertisement, etc., the intelligent surveillance video analysis technique has been proposed to gather the sufficient customer information and realize crowd counting and density map drawing. In this paper, a series of deep learning techniques are adopted to realize surveillance video analysis. This work covers different subproblems such as object detection, tracking and human identification. A skeleton recognition algorithm is adopted instead of object detection algorithm to overcome the severe occlusion problem. A multiple human tracking algorithm combing the human re-identification technology is adopted to realize the human tracking and counting. Finally, the density map and statistics information are obtained which can be used to evaluate and adjust the current business plan. A real store surveillance video is analyzed by the algorithm, and the results show the advantage of the algorithm.