IEEE Transactions on Circuits and Systems for Video Technology
Learning atomic human actions using variable-length Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
A survey on vision-based human action recognition
Image and Vision Computing
Efficient human action and gait analysis using multiresolution motion energy histogram
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Action and gait recognition from recovered 3-D human joints
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
A human action recognition algorithm based on semi-supervised kmeans clustering
Transactions on edutainment VI
Human attributes from 3D pose tracking
Computer Vision and Image Understanding
Fitting distal limb segments for accurate skeletonization in human action recognition
Journal of Ambient Intelligence and Smart Environments
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Efficient descriptor tree growing for fast action recognition
Pattern Recognition Letters
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Shape and kinematics are two important cues in human movement analysis. Due to real difficulties in extracting kinematics from videos accurately, this paper proposes to address the problem of human action recognition by spatiotemporal shape analysis. Without explicit feature tracking and complex probabilistic modeling of human movements, we directly convert an associated sequence of human silhouettes derived from videos into two types of computationally efficient representations, i.e., average motion energy and mean motion shape, to characterize actions. Supervised pattern classification techniques using various distance measures are used for recognition. The encouraging experimental results are obtained on a recent dataset including 10 different actions from 9 subjects.