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Analyzing Human Movements from Silhouettes Using Manifold Learning

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2 Author(s)
Liang Wang ; Monash University, Australia ; Suter, D.

A novel method for learning and recognizing sequential image data is proposed, and promising applications to vision-based human movement analysis are demonstrated. To find more compact representations of high-dimensional silhouette data, we exploit locality preserving projections (LPP) to achieve low-dimensional manifold embedding. Further, we present two kinds of methods to analyze and recognize learned motion manifolds. One is correlation matching based on the Hausdorrf distance, and the other is a probabilistic method using continuous hidden Markov models (HMM). Encouraging results are obtained in two representative experiments in the areas of human activity recognition and gait-based human identification.

Published in:

Video and Signal Based Surveillance, 2006. AVSS '06. IEEE International Conference on

Date of Conference:

Nov. 2006