Linear-projection-based classification of human postures in time-of-flight data
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Human motion recognition using Isomap and dynamic time warping
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Capturing human activity by a curve
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Human behavior classification by analyzing periodic motions
Frontiers of Computer Science in China
Learning video manifold for segmenting crowd events and abnormality detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
A speed invariant human identification system using gait biometrics
International Journal of Computational Vision and Robotics
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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.