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.

人工智能技术发展如此之快，并已应用于商业领域。为了预测顾客的喜好并调整产品或广告的位置等，提出了一种智能监控视频分析技术，以收集足够的顾客信息，实现人群计数和密度图绘制。本文采用了一系列的深度学习技术来实现监控视频分析。这项工作涵盖了不同的子问题，例如对象检测，跟踪和人工识别。采用骨架识别算法代替物体检测算法，克服了严重的遮挡问题。采用了结合人类重识别技术的多重人类跟踪算法来实现人类的跟踪和计数。最后，获得密度图和统计信息，可用于评估和调整当前的业务计划。该算法对真实商店监控录像进行了分析，结果表明了该算法的优越性。