Abstract
This paper presents a novel and efficient approach to improve performance of recognizing human actions from video by using an unorthodox combination of stage-level approaches. Feature descriptors obtained from dense trajectory i.e. HOG, HOF and MBH are known to be successful in representing videos. In this work, Fisher Vector Encoding with reduced dimensions are separately obtained for each of these descriptors and all of them are concatenated to form one super vector representing each video. To limit the dimension of this super vector we only include first order statistics, computed by the Gaussian Mixture Model, in the individual Fisher Vectors. Finally, we use elements of this super vector, as inputs to be fed to the Deep Belief Network (DBN) classifier. The performance of this setup is evaluated on KTH and Weizmann datasets. Experimental results show a significant improvement on these datasets. An accuracy of 98.92 and 100 % has been obtained on KTH and Weizmann dataset respectively.
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Dhar, P., Alvarez, J.M., Roy, P.P. (2017). Efficient Framework for Action Recognition Using Reduced Fisher Vector Encoding. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_31
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DOI: https://doi.org/10.1007/978-981-10-2107-7_31
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