基于步态的摄像机网络跨视域行人跟踪  

Gait based cross-view pedestrian tracking with camera network

作　　者：宋淑婕 万九卿[1] SONG Shujie;WAN Jiuqing(School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100191

出　　处：《北京航空航天大学学报》2023年第8期2154-2166,共13页Journal of Beijing University of Aeronautics and Astronautics

基　　金：北京市自然科学基金(4192031);国家自然科学基金(61873015)。

摘　　要：非重叠视域摄像机网络行人目标跨视域跟踪是智能视觉监控的基本问题之一。针对基于外观一致性假设的行人跨视域跟踪方法对光照或衣着变化敏感的问题,提出一种融合基于2D骨架图的步态特征与时空约束的跨视域行人跟踪方法。从单视域局部轨迹提取骨架集合计算步态特征,建立跨视域目标跟踪问题的整数线性规划模型,模型参数由步态特征相似度和时空约束定义,利用对偶分解算法实现问题的分布式求解。通过步态特征与更加精细化的时空约束融合,显著提升了仅基于步态特征的跨视域跟踪算法对于光照和衣着变化的鲁棒性,克服了单独使用步态或时空特征时判别力较弱的问题。在公开数据集上的测试结果表明,所提方法跟踪准确,且对光照和衣着变化具有鲁棒性。Pedestrian tracking across non-overlapping camera views is one of the basic problems of intelligent visual surveillance.A cross-view pedestrian target tracking method based on the gait features of a 2D skeleton diagram and space-time constraints is proposed in order to address the issue that the pedestrian cross-view tracking method based on the assumption of appearance consistency is sensitive to lighting or clothing changes.The skeleton set is extracted from the local trajectory of the single view to calculate the gait features,and the integer programming model of the cross-view target tracking problem is established.The model parameters are defined by the similarity of the gait features and the space-time constraints.The dual decomposition algorithm is used to realize the distributed solution to the above problems.The algorithm’s robustness to changes in lighting and clothing is greatly increased through the combination of gait features and more precise space-time restrictions,and it also solves the issue of weak discriminating when gait or space-time features are employed alone.The test results on the public data sets show that the proposed method is accurate in tracking and robust to lighting and clothing changes.

关 键 词：步态特征 时空约束 对偶分解 跨视域行人跟踪 智能视觉监控 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]