Title :
Integrating component cues for human pose tracking
Author :
Lee, Mun Wai ; Nevatia, Ramakant
Author_Institution :
Inst. for Robotics & Intelligent Syst., Southern California Univ., Los Angeles, CA, USA
Abstract :
Tracking human body pose in monocular video in the presence of image noise, imperfect foreground extraction and partial occlusion of the human body is important for many video analysis applications. Human pose tracking can be made more robust by integrating the detection of components such as face and limbs. We proposed an approach based on data-driven Markov chain Monte Carlo (DD-MCMC) where component detection results are used to generate state proposals for pose estimation and initialization. Experimental results on a realistic indoor video sequence show that the method is able to track a person during turning and sitting movements.
Keywords :
Markov processes; Monte Carlo methods; image sequences; object detection; tracking; video signal processing; component cues; data-driven Markov chain Monte Carlo; human pose tracking; image noise; monocular video; video analysis; video sequence; Face detection; Humans; Image analysis; Monte Carlo methods; Noise robustness; Proposals; State estimation; Tracking; Turning; Video sequences;
Conference_Titel :
Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. 2nd Joint IEEE International Workshop on
Print_ISBN :
0-7803-9424-0
DOI :
10.1109/VSPETS.2005.1570896