基于排队论的跨摄像头乘客轨迹识别方法

Queuing theory-based cross-camera passenger trajectory recognition method

目前摄像头监控视频群组中采用遍历查找搜索全部摄像头视频,或者拓扑网查找重复搜索,导致人员追踪查找效率低、准确性差,为解决这一问题,提出了一种基于排队论和顶点加权有向图的高效选择监控摄像机视频的方法。该方法借鉴排队论理论的原理,将摄像头视为顶点,并构建了一个加权有向图。通过计算权值,能够确定最优的监控路径,同时考虑了摄像头之间的连接和权值。这一方法的关键优势在于高效地选择监控摄像机视频。此外,将城市轨道交通节点目标乘客的最优运动路径与人员追踪相结合,采用了顶点加权有向图的思想,提高了识别的准确性和效率。研究结果表明,通过将排队论和顶点加权有向图理论应用于人员追踪领域,为解决实际问题和提升系统性能提供了一种创新的方法。这一方法对于提升监控系统性能和人员追踪能力具有重要意义。

Currently,in surveillance video groups,traditional methods for searching camera videos involve traversing and searching through all cameras or performing repetitive searches in a network topology.These approaches result in low efficiency and poor accuracy in tracking individuals.To address this issue,we propose an efficient method for selecting surveillance camera videos based on the principles of the queuing and vertex-weighted directed graph theories.In this method,we treat cameras as vertices and construct a weighted directed graph.By calculating weights,we can determine the optimal monitoring paths considering the connections and weights between cameras.The key advantage of this method is its efficient selection of surveillance camera videos.Additionally,by combining the optimal movement paths of target passengers in urban rail transit nodes with individual tracking,we use the concept of vertex-weighted directed graphs to enhance the accuracy and efficiency of person recognition.The research results show the great significance of this method in improving the performance of surveillance systems and individual tracking capabilities.By applying the queuing and vertex-weighted directed graph theories for individual tracking,we offer an innovative approach to address practical problems and enhance system performance.This method holds great importance in enhancing surveillance system performance and individual tracking capabilities.