NDLI: A statistical field model for pedestrian detection

Content Provider	IEEE Xplore Digital Library
Author	Ying Wu Ting Yu Gang Hua
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Northwestern Univ., Evanston, IL, USA (Ying Wu; Ting Yu; Gang Hua)
Abstract	This paper presents a new statistical model for detecting and tracking deformable objects such as pedestrians, where large shape variations induced by local shape deformation can not be well captured by global methods such as PCA. The proposed model employs a Boltzmann distribution to capture the prior of local deformation, and embeds it into a Markov network which can be learned from data. A mean field variational analysis of this model provides computationally efficient algorithms for computing the likelihood of image observations and facilitate fast model training. Based on that, effective detection and tracking algorithms for deformable objects are proposed and applied to pedestrian detection and tracking. The proposed method has several advantages. Firstly, it captures local deformation well and thus is robust to occlusions and clutter. In addition, it is computationally tractable. Moreover, it divides deformation into local deformation and global deformation, then conquers them by combining bottom-up and top-down methodologies. Extensive experiments demonstrate the effectiveness of the proposed model for deformable objects.
Sponsorship	IEEE Comput. Soc
Starting Page	1023
Ending Page	1030
File Size	576995
Page Count	8
File Format	PDF
ISBN	0769523722
ISSN	10636919
DOI	10.1109/CVPR.2005.49
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deformable models Object detection Shape Principal component analysis Boltzmann distribution Markov random fields Image analysis Algorithm design and analysis Computational modeling Robustness
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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