Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Rangarajan, Badrinarayanan and Radhakrishnan, Venkatesh Babu (2014) Human Action Recognition in Compressed Domain using PBL-McRBFN Approach. In: 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), APR 21-24, 2014, Singapore, SINGAPORE.

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Abstract

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

Item Type:	Conference Proceedings
Publisher:	IEEE
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords:	H.264/AVC; Human action recognition; Compressed domain video analysis; Motion vectors; Quantization parameters; Meta-cognitive learning; radial basis function network; PBL-McRBFN
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	31 Jul 2015 14:13
Last Modified:	31 Jul 2015 14:13
URI:	http://eprints.iisc.ac.in/id/eprint/51975

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