王斌, 王媛媛, 肖文华, 王炜, 张茂军. 基于判别稀疏编码视频表示的人体动作识别[J]. 机器人, 2012, 34(6): 745-750,757. DOI: 10.3724/SP.J.1218.2012.00745
引用本文: 王斌, 王媛媛, 肖文华, 王炜, 张茂军. 基于判别稀疏编码视频表示的人体动作识别[J]. 机器人, 2012, 34(6): 745-750,757. DOI: 10.3724/SP.J.1218.2012.00745
WANG Bin, WANG Yuanyuan, XIAO Wenhua, WANG Wei, ZHANG Maojun. Human Action Recognition Based on Discriminative Sparse Coding Video Representation[J]. ROBOT, 2012, 34(6): 745-750,757. DOI: 10.3724/SP.J.1218.2012.00745
Citation: WANG Bin, WANG Yuanyuan, XIAO Wenhua, WANG Wei, ZHANG Maojun. Human Action Recognition Based on Discriminative Sparse Coding Video Representation[J]. ROBOT, 2012, 34(6): 745-750,757. DOI: 10.3724/SP.J.1218.2012.00745

基于判别稀疏编码视频表示的人体动作识别

Human Action Recognition Based on Discriminative Sparse Coding Video Representation

  • 摘要: 为解决视频动作识别中“词袋”模型视频表示误差大、判别性弱,而影响人体动作识别精度的问题,本文提出判别稀疏编码视频表示算法:在稀疏编码框架下,引入Fisher判别分析,对视频局部时空特征编码,增强视频稀疏表示判别性.并提出在线判别字典学习算法,从海量视频数据中训练判别字典.实验表明,与现有算法相比,本文算法有效提高了人体动作识别精度.

     

    Abstract: The bag-of-words (BoW) model usually causes large errors and weak discrimination in video representation in video action recognition, and affects the human action recognition accuracy. To solve this problem, a discriminative sparse coding (DSC) video representation algorithm is proposed. It's a sparse coding framework involving a Fisher discriminative analysis to encode video local spatial-temporal features and increase the video sparse representation discrimination. And an online discriminative dictionary learning algorithm is also proposed to train a dictionary from massive video data. The experiments show that, comparing with the existing algorithms, the proposed algorithm effectively improves human action recognition accuracy.

     

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