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This paper presents a fast and simple method for human action recognition. The proposed technique relies on detecting interest points using SIFT (scale invariant feature transform) from each frame of the video. A fine-tuning step is used here to limit the number of interesting points according to the amount of details. Then the popular approach Bag of Video Words is applied with a new normalization technique. This normalization technique remarkably improves the results. Finally a multi class linear Support Vector Machine (SVM) is utilized for classification. Experiments were conducted on the KTH and Weizmann datasets. The results demonstrate that our approach outperforms most existing methods, achieving accuracy of 97.89% for KTH and 96.66% for Weizmann. | Journal of Advanced Research 2015 6 163-169 Cairo University Journal of Advanced Research ORIGINAL ARTICLE An enhanced method for human action recognition CrossMark Mona M. Moussa a Elsayed Hamayed b Magda B. Fayek b Heba A. El Nemr a a Computers and Systems Department Electronics Research Institute Egypt b Computer Engineering Department Faculty of Engineering Cairo University Egypt ARTICLE INFO ABSTRACT Article history Received 28 July 2013 Received in revised form 26 November 2013 Accepted 27 November 2013 Available online 5 December 2013 Keywords SIFT Action recognition Bag of words svM This paper presents a fast and simple method for human action recognition. The proposed technique relies on detecting interest points using SIFT scale invariant feature transform from each frame of the video. A fine-tuning step is used here to limit the number of interesting points according to the amount of details. Then the popular approach Bag of Video Words is applied with a new normalization technique. This normalization technique remarkably improves the results. Finally a multi class linear Support Vector Machine SVM is utilized for classification. Experiments were conducted on the KTH and Weizmann datasets. The results demonstrate that our approach outperforms most existing methods achieving accuracy of 97.89 for KTH and 96.66 for Weizmann. 2013 Production and hosting by Elsevier B.V. on behalf of Cairo University. Introduction Human action recognition is an active area of research due to the wide applications depending on it as detecting certain activities in surveillance video automatic video indexing and retrieval and content based video retrieval. Action representation can be categorized as flow based approaches 1 spatio-temporal shape template based approaches 2 3 tracking based approaches 4 and interest points based approaches 5 . In flow based approaches optical flow computation is used to describe motion it is sensitive to noise and cannot reveal the true motions.
