The paper focuses on developing the system for people flow recognition based on many video camera images. The project arose based on the real need — its application in the city malls to find the most visited places and support customers’ campaigns. The potential application in other domains is possible (public buildings, airports). In contrast to the existing solutions, our approach involves on-edge image analysis to decrease the risk of data loss and the system cost. In the project, we design the whole processing pipeline composed of the component modules responsible for detecting, tracking, and reidentifying (shuffling) people. Our experimental platform enabled us to compare multiple method variants for each module. Based on extensive experimental research, the final solution uses the pretrained SSD method in the detection module. The centroid algorithm applied to displacement vectors combined with the Siamese network is the basis for the object tracking module. The best model to solve the reidentification task is the Resnet50. For the Market 1501 dataset, it achieved Rank-1 efficiency of 84.6%. The system gives a visualization of the main paths of people’s movements in the form of a heat map and assigns the direction where people most often look. In the experimental study, we assessed the system’s effectiveness and time efficiency, and the current results give a perspective for its commercialization in the nearest future.

本文重点研究了基于大量摄像机图像的人流识别系统。该项目是基于真正的需求而产生的——它在城市购物中心的应用，以寻找访问量最大的地方并支持客户的活动。其他领域的潜在应用是可能的（公共建筑、机场）。与现有解决方案相比，我们的方法涉及边缘图像分析，以降低数据丢失的风险和系统成本。在项目中，我们设计了由负责检测、跟踪和重新识别（改组）人员的组件模块组成的整个处理流水线。我们的实验平台使我们能够比较每个模块的多种方法变体。基于大量的实验研究，最终的解决方案在检测模块中使用了预训练的 SSD 方法。应用于位移向量的质心算法结合 Siamese 网络是目标跟踪模块的基础。解决重识别任务的最佳模型是 Resnet50。对于 Market 1501 数据集，它实现了 84.6% 的 Rank-1 效率。该系统以热图的形式显示人们运动的主要路径，并指定人们最常看的方向。在实验研究中，我们评估了系统的有效性和时间效率，目前的结果为其在不久的将来商业化提供了前景。该系统以热图的形式可视化人们运动的主要路径，并指定人们最常看的方向。在实验研究中，我们评估了系统的有效性和时间效率，目前的结果为其在不久的将来商业化提供了前景。该系统以热图的形式显示人们运动的主要路径，并指定人们最常看的方向。在实验研究中，我们评估了系统的有效性和时间效率，目前的结果为其在不久的将来商业化提供了前景。