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Object Tracking across Non-overlapping Cameras using Adaptive Models
Xiaotang Chen; Huang Kaiqi; Tieniu Tan; Kaiqi Huang
2012
会议名称ACCV Workshop on Detection and Tracking in Challenging Environments (DTCE)
会议录名称International Conference on Computer Vision, 2012
页码464-477
会议日期2012
会议地点China
摘要In this paper, we propose a novel approach to track multiple objects across non-overlapping cameras, which aims at giving each object a unique label during its appearance in the whole multi-camera system. We formulate the problem of the multiclass object recognition as a binary classification problem based on an AdaBoost classifier. As the illumination variance, viewpoint changes, and camera characteristic changes vary with camera pairs, appearance changes of objects across different camera pairs generally follow different patterns. Based on this fact, we use a categorical variable indicating the entry/exit cameras as a feature to deal with different patterns of appearance changes across cameras. For each labeled object, an adaptive model describing the intraclass similarity is computed and integrated into a sequence based matching framework, depending on which the final matching decisions are made. Multiple experiments are performed on different datasets. Experimental results demonstrate the effectiveness of the proposed method.
关键词Object Tracking
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12693
专题智能感知与计算研究中心
通讯作者Kaiqi Huang
作者单位中国科学院自动化研究所
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GB/T 7714
Xiaotang Chen,Huang Kaiqi,Tieniu Tan,et al. Object Tracking across Non-overlapping Cameras using Adaptive Models[C],2012:464-477.
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