Welcome to the InfoSci Platform
Reference Hub1
Human Motion Tracking in Video: A Practical Approach

Human Motion Tracking in Video: A Practical Approach

Tony Tung, Takashi Matsuyama
Copyright: © 2010 |Pages: 13
ISBN13: 9781605669007|ISBN10: 1605669008|ISBN13 Softcover: 9781616922160|EISBN13: 9781605669014
DOI: 10.4018/978-1-60566-900-7.ch001
Cite Chapter Cite Chapter

MLA

Tung, Tony, and Takashi Matsuyama. "Human Motion Tracking in Video: A Practical Approach." Machine Learning for Human Motion Analysis: Theory and Practice, edited by Liang Wang, et al., IGI Global, 2010, pp. 1-13. https://doi.org/10.4018/978-1-60566-900-7.ch001

APA

Tung, T. & Matsuyama, T. (2010). Human Motion Tracking in Video: A Practical Approach. In L. Wang, L. Cheng, & G. Zhao (Eds.), Machine Learning for Human Motion Analysis: Theory and Practice (pp. 1-13). IGI Global. https://doi.org/10.4018/978-1-60566-900-7.ch001

Chicago

Tung, Tony, and Takashi Matsuyama. "Human Motion Tracking in Video: A Practical Approach." In Machine Learning for Human Motion Analysis: Theory and Practice, edited by Liang Wang, Li Cheng, and Guoying Zhao, 1-13. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-900-7.ch001

Export Reference

Mendeley
Favorite

Abstract

This chapter presents a new formulation for the problem of human motion tracking in video. Tracking is still a challenging problem when strong appearance changes occur as in videos of humans in motion. Most trackers rely on a predefined template or on a training dataset to achieve detection and tracking. Therefore they are not efficient to track objects whose appearance is not known in advance. A solution is to use an online method that updates iteratively a subspace of reference target models. In addition, we propose to integrate color and motion cues in a particle filter framework to track human body parts. The algorithm process consists of two modes, switching between detection and tracking. The detection steps involve trained classifiers to update estimated positions of the tracking windows, whereas tracking steps rely on an adaptive color-based particle filter coupled with optical flow estimations. The Earth Mover distance is used to compare color models in a global fashion, and constraints on flow features avoid drifting effects. The proposed method has revealed its efficiency to track body parts in motion and can cope with full appearance changes. Experiments were performed on challenging real world videos with poorly textured models and non-linear motions.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.