A field model for human detection and tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Ying Wu, Ting Yu

Abstract

The large shape variability and partial occlusions challenge most object detection and tracking methods for nonrigid targets such as pedestrians. This paper presents a new approach based on a two-layer statistical field model that characterizes the prior of the complex shape variations as a Boltzmann distribution and embeds this prior and the complex image likelihood into a Markov field. A probabilistic variational analysis of this model reveals a set of fixed-point equations characterizing the equilibrium of the field. It leads to computationally efficient methods for calculating the image likelihood and for training the model. Based on that, effective algorithms for detecting nonrigid objects are developed. This new approach has several advantages. First, it is intrinsically suitable for capturing local nonrigidity. In addition, due to the distributed likelihood, this approach is robust to partial occlusions. Moreover, the two-layer structure provides large flexibility of modeling the image observations, which makes the new method robust to clutters. Extensive experiments demonstrate its effectiveness.

References

Jun 1, 1984·IEEE Transactions on Pattern Analysis and Machine Intelligence·S Geman, D Geman
Jan 1, 1991·Journal of Cognitive Neuroscience·A L Yuille

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Citations

Oct 17, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Markus Enzweiler, Dariu M Gavrila
Sep 2, 2010·IEEE Transactions on Neural Networks·Jialue FanYihong Gong
Dec 12, 2012·Critical Reviews in Food Science and Nutrition·Qurat Ul Ain Riaz, Tariq Masud

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