NDLI: Body Localization in Still Images Using Hierarchical Models and Hybrid Search

Content Provider	IEEE Xplore Digital Library
Author	Jiayong Zhang Jiebo Luo Collins, R. Yanxi Liu
Copyright Year	2006
Description	Author affiliation: Carnegie Mellon Univ., PA (Jiayong Zhang)
Abstract	We present a 3-level hierarchical model for localizing human bodies in still images from arbitrary viewpoints. We first fit a simple tree-structured model defined on a small landmark set along the body contours by Dynamic Programming (DP). The output is a series of proposal maps that encode the probabilities of partial body configurations. Next, we fit a mixture of view-dependent models by Sequential Monte Carlo (SMC), which handles self-occlusion, anthropometric constraints, and large viewpoint changes. DP and SMC are designed to search in opposite directions such that the DP proposals are utilized effectively to initialize and guide the SMC inference. This hybrid strategy of combining deterministic and stochastic search ensures both the robustness and efficiency of DP, and the accuracy of SMC. Finally, we fit an expanded mixture model with increased landmark density through local optimization. The model hierarchy is trained on a large number of gait images. Extensive tests on cluttered images with varying poses including walking, dancing and various types of sports activities demonstrate the feasibility of the proposed approach.
Starting Page	1536
Ending Page	1543
File Size	981073
Page Count	8
File Format	PDF
ISBN	0769525970
ISSN	10636919
DOI	10.1109/CVPR.2006.72
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sliding mode control Biological system modeling Proposals Humans Dynamic programming Monte Carlo methods Stochastic processes Robustness Testing Legged locomotion
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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