Abstract
Practical applications of computer vision in smart cities usually assume system integration and operation in challenging open-world environments. In the case of person re-identification task the main goal is to retrieve information whether the specific person has appeared in another place at a different time instance of the same video, or over multiple camera feeds. This typically assumes collecting raw data from video surveillance cameras in different places and under varying illumination conditions. In the considered open-world setting it also requires detection and localization of the person inside the analyzed video frame before the main re-identification step. With multi-person and multi-camera setups the system complexity becomes higher, requiring sophisticated tracking solutions and re-identification models. In this work we will discuss existing challenges in system design architectures, consider possible solutions based on different computer vision techniques, and describe applications of such systems in retail stores and public spaces for improved marketing analytics. In order to analyse sensitivity of person re-identification task under different open-world environments, a performance of one close to real-time solution will be demonstrated over several video captures and live camera feeds. Finally, based on conducted experiments we will indicate further research directions and possible system improvements.
Abstract (translated)
计算机视觉在智慧城市中的实际应用通常假设系统会在具有挑战性的开放世界环境中进行集成和运行。以人员重识别任务为例，主要目标是从同一视频的不同时段或不同摄像机流中检索特定人物是否出现在另一个地方的信息。这通常需要从不同地点、在不同的光照条件下收集监控摄像头的原始数据。在这种开放世界的设定下，还需要在分析的视频帧内检测和定位人物，然后才能进行主要的重识别步骤。当涉及多人和多相机设置时，系统的复杂性会增加，需要更复杂的跟踪解决方案和重识别模型。 在这项工作中，我们将讨论系统设计架构中存在的现有挑战，并基于不同的计算机视觉技术考虑可能的解决方案。此外，我们还将描述此类系统在零售商店和公共场所的应用，以改进市场营销分析。为了分析人员重识别任务在不同开放世界环境下的敏感性，将演示一种接近实时的解决方案在多个视频片段和现场摄像机流中的性能表现。最后，根据进行的实验，我们将指出进一步的研究方向以及可能的系统改进措施。 这一工作不仅涵盖了技术挑战，还强调了计算机视觉如何用于解决实际问题，特别是在智慧城市环境中提升数据收集、分析及应用的有效性方面。
URL
https://arxiv.org/abs/2505.00772
