Publication: Inter and Intra Class Correlation Analysis (IIcCA) for Human Action Recognition in Realistic Scenarios
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2017-07-11
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The Institution Of Engineering And Technology
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Abstract
Human action recognition in realistic scenarios is an important yet challenging task. In this paper we propose a new method, Inter and Intra class correlation analysis (IICCA), to handle inter and intra class variations observed in realistic scenarios. Our contribution includes learning a class specific visual representation that efficiently represents a particular action class and has a high discriminative power with respect to other action classes. We use statistical measures to extract visual words that are highly intra correlated and less inter correlated. We evaluated and compared our approach with state-of-the-art work using a realistic benchmark human action recognition dataset.
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This paper has been presented at : 8th International Conference of Pattern Recognition Systems (ICPRS 2017)
Keywords
Human action recognition, Inter and intra class variation, Correlation analysis, UCF Sports
Bibliographic citation
Nazir, S., Yousaf, M.H y Velastin, S.A. (2017). Inter and Intra class correlation analysis (IIcCA) for human action recognition in realistic scenarios. In 8th International Conference of Pattern Recognition Systems (ICPRS 2017)