Computer Science ›› 2017, Vol. 44 ›› Issue (2): 306-308.doi: 10.11896/j.issn.1002-137X.2017.02.052

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Activity Recognition from Depth Image Sequences Based on L2,1-norm Sparse Feature Selection and Super Normal Vector

SONG Xiang-fa, ZHANG Yan-feng and ZHENG Feng-bin   

  • Online:2018-11-13 Published:2018-11-13

Abstract: This paper presented a novel method of activity recognition from depth image sequences based on L2,1-norm sparse feature selection and super normal vector.First,the super normal vector feature is extracted from depth image sequences.Then the most discriminative feature subset is selected from the whole super normal vector feature set based on the method of L2,1-norm sparse feature selection.Finally,the classification is based on Liblinear classifier.Experimental results on MSR Action3D dataset show that the proposed method achieves 94.55% of recognition accuracy using only 2% of the whole super normal vector feature,and is superior to the state-of-art methods.

Key words: Activity recognition,Depth image sequences,Super normal vector,Sparse feature selection,L2,1-norm

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