Action recognition using multi-layer Depth Motion maps and Sparse Dictionary Learning

In this paper, we propose a new spatio-temporal feature based method for human action recognition using depth image sequence. Fist, Layered Depth Motion maps (LDM) are utilized to capture the temporal motion feature. Next, multi-scale HOG descriptors are computed on LDM to characterize the structura...

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Bibliographic Details
Published in2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) pp. 1 - 6
Main Authors Chengwu Liang, Enqing Chen, Lin Qi, Ling Guan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2015
Subjects
Dictionaries
Encoding
Feature extraction
Image recognition
Skeleton
Three-dimensional displays
Training
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Summary:In this paper, we propose a new spatio-temporal feature based method for human action recognition using depth image sequence. Fist, Layered Depth Motion maps (LDM) are utilized to capture the temporal motion feature. Next, multi-scale HOG descriptors are computed on LDM to characterize the structural information of actions. Then sparse coding is applied for feature representation. Extending Sparse fisher Discriminative Dictionary Learning (SDDL) model and its corresponding classification scheme are also introduced. In SDDL model, the sub-dictionary is updated class by class, leading to class-specific compact discriminative dictionaries. The proposed method is evaluated on public MSR Action3D datasets and demonstrates great performance, especially in cross subject test.
DOI:10.1109/MMSP.2015.7340790