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Efficient compression and network adaptive video coding for distributed video surveillance

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Abstract

The availability of cheap network based video cameras and the prevalence of wireless networks has lead to a major thrust towards deployment of large scale Distributed Video Surveillance (DVS) systems. This has opened up an important area of research to deal with the issues involved in DVS system for efficient collection and transmission of large scale video streams from the cameras at the guarded sites, to the end users in possibly constrained network conditions. In this paper, we propose a framework based on content-based video classification and scalable compression scheme to provide a robust bandwidth efficient video transmission for DVS. The scheme builds on a Discrete Wavelet Transform (DWT) based Color-Set Partitioning for Hierarchical Trees (CSPIHT) coding to obtain a scalable bitstream. Wavelet domain segmentation and compression assists in development of a DVS architecture. The architecture includes a novel module for dynamic allocation of Network bandwidth based on the current available resources and constraints. Different frame constituents are optimally coded based on their relative significance, perceptual quality, and available estimate of network bandwidth. Experimental result over different video sequences and simulations for Network conditions demonstrate the efficient performance of the approach.

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Authors and Affiliations

  1. Dept. of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engg. and Tech., Hyderabad, India

    Praveen Kumar

  2. Department of Electrical and Computer Engineering, Iowa State University, 2215 Coover Hall, Ames, IA, 50011, USA

    Amit Pande

  3. Department of Computer Science and Engineering, College of Engineering Roorkee, Roorkee, India

    Ankush Mittal

Authors
  1. Praveen Kumar
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  2. Amit Pande
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  3. Ankush Mittal
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Correspondence to Praveen Kumar.

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Kumar, P., Pande, A. & Mittal, A. Efficient compression and network adaptive video coding for distributed video surveillance. Multimed Tools Appl 56, 365–384 (2012). https://doi.org/10.1007/s11042-010-0672-2

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  • DOI: https://doi.org/10.1007/s11042-010-0672-2

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