Recognition of human activities using a multiclass relevance vector machine
Yow, Kin Choong
Date of Issue2012
School of Computer Engineering
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.
DRNTU::Engineering::Computer science and engineering
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). This paper was published in Optical Engineering and is made available as an electronic reprint (preprint) with permission of Society of Photo-Optical Instrumentation Engineers (SPIE). The paper can be found at the following official DOI: [http://dx.doi.org/10.1117/1.OE.51.1.017202]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.