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Compressed domain human action recognition in H.264/AVC video streams

Tom, Manu and Babu, Venkatesh R and Praveen, Gnana R (2015) Compressed domain human action recognition in H.264/AVC video streams. In: MULTIMEDIA TOOLS AND APPLICATIONS, 74 (21). pp. 9323-9338.

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Official URL: http://dx.doi.org/10.1007/s11042-014-2083-2

Abstract

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

Item Type: Journal Article
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Additional Information: Copy right for this article belongs to the SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
Keywords: H.264/AVC; Human action recognition; Compressed domain video analysis; Motion vectors; Quantization parameters
Department/Centre: Division of Information Sciences > Supercomputer Education & Research Centre
Date Deposited: 30 Oct 2015 06:54
Last Modified: 30 Oct 2015 06:54
URI: http://eprints.iisc.ernet.in/id/eprint/52579

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