Effective Active Skeleton Representation for Low Latency Human Action Recognition

With the development of depth sensors, low latency 3D human action recognition has become increasingly important in various interaction systems, where response with minimal latency is a critical process. High latency not only significantly degrades the interaction experience of users, but also makes...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2016-02, Vol.18 (2), p.141-154
Main Authors: Cai, Xingyang, Zhou, Wengang, Wu, Lei, Luo, Jiebo, Li, Houqiang
Format: Article
Language:English
Subjects:
Acceleration
Action recognition
depth camera
Feature extraction
Human
Human behavior
Limbs
low latency
Markov random field
Markov random fields
Mathematical models
Multimedia
multiple instance learning
Noise measurement
Recognition
Representations
Skeleton
Three dimensional
Three-dimensional displays
Videos
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Summary:With the development of depth sensors, low latency 3D human action recognition has become increasingly important in various interaction systems, where response with minimal latency is a critical process. High latency not only significantly degrades the interaction experience of users, but also makes certain interaction systems, e.g., gesture control or electronic gaming, unattractive. In this paper, we propose a novel active skeleton representation towards low latency human action recognition . First, we encode each limb of the human skeleton into a state through a Markov random field. The active skeleton is then represented by aggregating the encoded features of individual limbs. Finally, we propose a multi-channel multiple instance learning with maximum-pattern-margin to further boost the performance of the existing model. Our method is robust in calculating features related to joint positions, and effective in handling the unsegmented sequences. Experiments on the MSR Action3D, the MSR DailyActivity3D, and the Huawei/3DLife-2013 dataset demonstrate the effectiveness of the model with the proposed novel representation, and its superiority over the state-of-the-art low latency recognition approaches.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2015.2505089