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Báo cáo hóa học: " Research Article An Action Recognition Scheme Using Fuzzy Log-Polar Histogram and Temporal Self-Similarity"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An Action Recognition Scheme Using Fuzzy Log-Polar Histogram and Temporal Self-Similarity | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 540375 9 pages doi 10.1155 2011 540375 Research Article An Action Recognition Scheme Using Fuzzy Log-Polar Histogram and Temporal Self-Similarity Samy Sadek 1 Ayoub Al-Hamadi 1 Bernd Michaelis 1 and Usama Sayed2 1 Institute for Electronics Signal Processing and Communications IESK Otto-von-Guericke University Magdeburg 39106 Magdeburg Germany 2Electrical Engineering Department Assiut University Assiut Egypt Correspondence should be addressed to Samy Sadek samy.bakheet@ovgu.de Received 25 July 2010 Revised 26 October 2010 Accepted 8 January 2011 Academic Editor Mark Liao Copyright 2011 Samy Sadek et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Temporal shape variations intuitively appear to provide a good cue for human activity modeling. In this paper we lay out a novel framework for human action recognition based on fuzzy log-polar histograms and temporal self-similarities. At first a set of reliable keypoints are extracted from a video clip i.e. action snippet . The local descriptors characterizing the temporal shape variations of action are then obtained by using the temporal self-similarities defined on the fuzzy log-polar histograms. Finally the SVM classifier is trained on these features to realize the action recognition model. The proposed method is validated on two popular and publicly available action datasets. The results obtained are quite encouraging and show that an accuracy comparable or superior to that of the state-of-the-art is achievable. Furthermore the method runs in real time and thus can offer timing guarantees to real-time applications. 1. Introduction Human action recognition has received and still receives considerable attention in the field of computer vision due to its .