Recognition of human activities using a multiclass relevance vector machine

Weihua He; Yongcai Guo; Kin Choong Yow

doi:10.1117/1.OE.51.1.017202

6 February 2012 Recognition of human activities using a multiclass relevance vector machine

Weihua He, Yongcai Guo, Kin Choong Yow

Author Affiliations +

Funded by: Chinese Ministry of Education, Chongqing Municipal Natural Science Foundation of China

Optical Engineering, Vol. 51, Issue 1, 017202 (February 2012). https://doi.org/10.1117/1.OE.51.1.017202

Abstract

We address the issue of human activity recognition by introducing the multiclass relevance vector machine (mRVM), the current state-of-the-art kernel machine learning technology given the multiclass classification problems (actually, activity recognition can commonly be viewed as a multiclass classification problem). Under our proposed recognition framework, the required procedure consists of three functional cascade modules: a. detecting the human silhouette blobs from the image sequence by the background subtraction method; b. extracting the shape and the motion features from the variation energy image (VEI); and c. sending the obtained features to the mRVM and recognizing the human activity. There are two types of mRVM: the constructive mRVM₁ and the top-down mRVM₂. We performed 10 times three-fold cross-validation on the Weizmann benchmark data set to examine the effectiveness of the proposed method. We also compared our method with other existing approaches, and the experimental results show that the proposed method offers superior performance. In summary, the mRVM, especially the mRVM₂, has advantages both in terms of recognition rate and sparsity, along with a simple feature extraction process. The mRVM also significantly simplifies the classification process, by comparison with traditional binary-tree style multiclass classifiers.

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Weihua He, Yongcai Guo, and Kin Choong Yow "Recognition of human activities using a multiclass relevance vector machine," Optical Engineering 51(1), 017202 (6 February 2012). https://doi.org/10.1117/1.OE.51.1.017202

Published: 6 February 2012

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