Non-rigid object tracking using adaptive part-based model

Xiaohui Shen; Jin Zhang; Jie Zhou; Gang Rong

doi:10.1117/12.749830

15 November 2007 Non-rigid object tracking using adaptive part-based model

Xiaohui Shen, Jin Zhang, Jie Zhou, Gang Rong

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67862Y (2007) https://doi.org/10.1117/12.749830
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

This paper presents an adaptive part-based probabilistic model for non-rigid object tracking. Without any assumption on scenes or poses, our model is online generated and updated. The parts in the model are extracted by clustering based on the appearance consistency of local feature descriptors in the object. A probability indicating the possibility of a part belonging to the object is then assigned to each part and adapted during tracking. We also propose a fully automatic algorithm for single object tracking with model matching and adaption. Our approach is evaluated on three different datasets and compared with previous work on visual tracking. The experimental results showed that our approach can track non-rigid object under occlusion and object deformation effectively in real time. Moreover, it works even if the target is partially occluded at initialization step.

Citation Download Citation

Xiaohui Shen, Jin Zhang, Jie Zhou, and Gang Rong "Non-rigid object tracking using adaptive part-based model", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67862Y (15 November 2007); https://doi.org/10.1117/12.749830

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KEYWORDS

Detection and tracking algorithms

Optical tracking

Positron emission tomography

Automatic tracking

Performance modeling

Visual process modeling

Image processing

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