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Object Tracking across Non-overlapping Cameras using Adaptive Models
Xiaotang Chen; Huang Kaiqi; Tieniu Tan; Kaiqi Huang
Conference NameACCV Workshop on Detection and Tracking in Challenging Environments (DTCE)
Source PublicationInternational Conference on Computer Vision, 2012
Conference Date2012
Conference PlaceChina
AbstractIn 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.
KeywordObject Tracking
Document Type会议论文
Corresponding AuthorKaiqi Huang
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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