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Recognizing human activities using appearance metric feature and kinematics feature

[+] Author Affiliations
Huimin Qian, Jun Zhou, Xinbiao Lu

Hohai University, College of Energy and Electrical Engineering, Nanjing, China

Xinye Wu

China United Network Communications Corporation, Jiangsu Branch, Nanjing, China

J. Electron. Imaging. 26(3), 033015 (May 23, 2017). doi:10.1117/1.JEI.26.3.033015
History: Received October 17, 2016; Accepted May 3, 2017
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Abstract.  The problem of automatically recognizing human activities from videos through the fusion of the two most important cues, appearance metric feature and kinematics feature, is considered. And a system of two-dimensional (2-D) Poisson equations is introduced to extract the more discriminative appearance metric feature. Specifically, the moving human blobs are first detected out from the video by background subtraction technique to form a binary image sequence, from which the appearance feature designated as the motion accumulation image and the kinematics feature termed as centroid instantaneous velocity are extracted. Second, 2-D discrete Poisson equations are employed to reinterpret the motion accumulation image to produce a more differentiated Poisson silhouette image, from which the appearance feature vector is created through the dimension reduction technique called bidirectional 2-D principal component analysis, considering the balance between classification accuracy and time consumption. Finally, a cascaded classifier based on the nearest neighbor classifier and two directed acyclic graph support vector machine classifiers, integrated with the fusion of the appearance feature vector and centroid instantaneous velocity vector, is applied to recognize the human activities. Experimental results on the open databases and a homemade one confirm the recognition performance of the proposed algorithm.

© 2017 SPIE and IS&T

Citation

Huimin Qian ; Jun Zhou ; Xinbiao Lu and Xinye Wu
"Recognizing human activities using appearance metric feature and kinematics feature", J. Electron. Imaging. 26(3), 033015 (May 23, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.3.033015


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