Human tracking in camera network with non-overlapping FOVs  

在非重叠视域监控网络中的人体目标跟踪(英文)

作　　者：林国余[1] 杨彪[1] 张为公[1] 

机构地区：[1]东南大学仪器科学与工程学院,南京210096

出　　处：《Journal of Southeast University(English Edition)》2012年第2期156-163,共8页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No. 60972001 );the Science and Technology Plan of Suzhou City(No. SG201076)

摘　　要：An adaptive human tracking method across spatially separated surveillance cameras with non-overlapping fields of views （FOVs） is proposed. The method relies on the two cues of the human appearance model and spatio-temporal information between cameras. For the human appearance model, an HSV color histogram is extracted from different human body parts （head, torso, and legs）, then a weighted algorithm is used to compute the similarity distance of two people. Finally, a similarity sorting algorithm with two thresholds is exploited to find the correspondence. The spatio- temporal information is established in the learning phase and is updated incrementally according to the latest correspondence. The experimental results prove that the proposed human tracking method is effective without requiring camera calibration and it becomes more accurate over time as new observations are accumulated.针对存在非重叠视野的摄像机监控网络,提出了一种基于人体外观模型和摄像机间时空信息的人体目标自适应跟踪算法. 对于人体外观模型,首先根据人体测量学理论将人体目标划分成头、躯干和腿 3 个部分,分别提取各部分的 HSV 颜色直方图特征用于构建人体外观模型,然后引入加权因子计算人体目标之间的相似度,最后采用一种基于双阈值的相似度排序算法确定人体目标的匹配关系. 对于摄像机间的时空信息,通过增量学习,不断积累目标关联信息,经统计分析逐步更新摄像机间时空信息. 实验结果验证了所提出的跟踪算法在无需摄像机标定的条件下能够实现人体目标的连续跟踪,且随着关联匹配信息的累加,算法的跟踪准确性也逐步提高.

关 键 词：multiple camera tracking non-overlapping FOVs spatio-temporal information human appearance model incremental learning 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]