Authors:
Ahmad K. N. Tehrani
;
Maryam Asadi Aghbolaghi
and
Shohreh Kasaei
Affiliation:
Sharif University of Technology, Iran, Islamic Republic of
Keyword(s):
Human Action Recognition, Active Joints, Hidden Marcov Model (HMM), Skeletal Human Body Model.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
Abstract:
A novel method for human action recognition from the sequence of skeletal data is presented in this paper.
The proposed method is based on the idea that some of body joints are inactive and do not have any physical
meaning during performing an action. In other words, regardless of the subjects that perform an action, for
each action only a certain set of joints are meaningfully involved. Consequently, extracting features from
inactive joints is a time-consuming task. To cope with this problem, in this paper, only the dynamic of active
joints is modeled. To consider the local temporal information, a sliding window is used to divide the trajectory
of active joints into some consecutive windows. Feature extraction is then applied on all windows of active
joints’ trajectories and then by using the K-means clustering all features are quantized. Since each action has
its own active joints, in this paper one-vs-all classification strategy is exploited. Finally, to take into account
the glob
al motion information, the consecutive quantized features of the samples of an action are fed into the
hidden Markov model (HMM) of that action. The experimental results show that using active joints can get
96% of maximum reachable accuracy from using all joints.
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