ABSTRACT 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.

中文翻译：

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基于深度学习技术的店铺监控视频分析

摘要 AI 技术发展如此之快，已经应用到商业领域。为了预测顾客偏好，调整产品或广告等的摆放位置，提出了智能监控视频分析技术，收集足够的顾客信息，实现人群计数和密度图绘制。本文采用一系列深度学习技术实现监控视频分析。这项工作涵盖了不同的子问题，例如目标检测、跟踪和人类识别。采用骨架识别算法代替目标检测算法来克服严重的遮挡问题。采用结合人体再识别技术的多人体跟踪算法，实现人体跟踪计数。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。算法对一段真实的门店监控视频进行了分析，结果表明了算法的优越性。