融合RGB-D多模特征和半径边缘约束超限学习的动作识别

doi:10.16451/j.cnki.issn1003-6059.201810010

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Abstract

An exemplars multiple kernel learning-extreme learning machine(MKL-ELM) based human action recognition approach with multi-modal visual feature fusion from RGB-D videos is proposed to solve the problem of single modal visual feature with the limited discrimination ability for all categories of human actions. Firstly, the robust and dense moving pose features with human skeleton surface fitting and dense trajectories from human motion are extracted. The sparse histogram of oriented principal component(SHOPC) features of 3D body geometry with the normal plane of dense point clouds is perceived and the histogram of 3D gradient orientation(HOG3D) features embedded with human appearance textures on spatial temporal neighbor of body nodes in the given videos is extracted. The modified MKL-ELM with radius-margin bound is exploited to fuse the given multi-modal visual features. Then, the set of the representative exemplars for each human action is mined with the contrast data technique. Finally, each sample is hierarchically classified by the designed exemplars-MKL-ELM model with greedy prediction strategy to recognize the human actions with the fused features and the given exemplars. The experiments show that compared with the traditional methods, the proposed action recognition method has significant advantages with high classification accuracy and computational efficiency.

Received: 24 January 2018

ZTFLH:

TP 391.41

Fund:

Supported by National Natural Science Foundation of China(No.61001152,31200747,61071091,61071166,61172118), Natural Science Foundation of Jiangsu Province(No.BK2012437), Natural Science Foundation of NJUPT(No.NY214037), China Scholarship Council Program

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	Articles by authors
	LIU Tianliang
	CHEN Kehu
	DAI Xiubin
	LUO Jiebo

Cite this article:

LIU Tianliang,CHEN Kehu,DAI Xiubin等. Action Recognition Fused with RGB-D Multi-modal Features and Radius-Margin Bounded Extreme Learning[J]. , 2018, 31(10): 958-964.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201810010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I10/958

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