Exploiting pair-wise constraints between parts for human tracking

Jin Zhang; Xiaohui Shen; Jie Zhou; Gang Rong

doi:10.1117/12.748652

15 November 2007 Exploiting pair-wise constraints between parts for human tracking

Jin Zhang, Xiaohui Shen, Jie Zhou, Gang Rong

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880X (2007) https://doi.org/10.1117/12.748652
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Human tracking has attracted much attention from the researchers in the fields of computer vision and pattern recognition. The problem is generally extremely challenging partly because human bodies are articulated and versatile, and partly because background clutter, both of which demand a strong human model. However, there is usually a trade-off between the discriminative power and the complexity of a given model. This paper presents a simple yet distinctive appearance model for real time human tracking by exploiting the pairwise constraints between parts. The parts in our model are generated online by sampling the foreground of the scene into overlapping blocks and grouping them into appearance coherent parts with mean shift algorithm. Constraints between the resulting parts are defined and used to encode the structure of human body. To tolerate the possible human deformations and occlusions, the model is layered. With this model, we design an algorithm for human tracking and test its performance on real world image sequences. Experimental results show that the proposed appearance model although simple, has enough discriminative power to classify multiple humans even in presence of occlusions and the associated tracking method can run in real time.

Citation Download Citation

Jin Zhang, Xiaohui Shen, Jie Zhou, and Gang Rong "Exploiting pair-wise constraints between parts for human tracking", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880X (15 November 2007); https://doi.org/10.1117/12.748652

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KEYWORDS

Motion models

Visual process modeling

Lithium

Detection and tracking algorithms

Video surveillance

Motion analysis

Computer vision technology

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