Segmentation and tracking of multiple humans in crowded environments.

Full Abstract

Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model based approach to interpret the image observations by multiple, partially occluded human hypotheses in a Bayesian framework. We define a joint image likelihood for multiple humans based on the appearance of the humans, the visibility of body obtained by occlusion reasoning, and foreground/background separation. The optimal solution is obtained by using an efficient sampling method, data-driven Markov chain Monte Carlo (DDMCMC), which uses image observations for proposal probabilities. Knowledge of various aspects including human shape, camera model, and image cues are integrated in one theoretically sound framework. We present experimental results and quantitative evaluation, demonstrating that the resulting approach is effective for very challenging data.

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Author information

Author/s: Zhao, Tao (T); Nevatia, Ram (R); Wu, Bo (B);

Affiliation: Intuitive Surgical Inc., Sunnyvale, CA 94086, USA. taozhao(-atsign-)alumni.usc.edu

Journal and publication information

Publication Type: Evaluation Studies; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2008-Jul; vol 30 (issue 7) : pp 1198-211

Dates: Created 2008/06/13; Completed 2008/07/10;

PMID: 18550903, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Biometry - methods
Environment
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods

Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Movement - physiology
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique

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