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H.264 compressed video classification using Histogram of Oriented Motion Vectors (HOMV)

Sovan Biswas,R. Venkatesh Babu

Published 2013 in IEEE International Conference on Acoustics, Speech, and Signal Processing

ABSTRACT

In this paper, we have proposed a simple and effective approach to classify H.264 compressed videos, by capturing orientation information from the motion vectors. Our major contribution involves computing Histogram of Oriented Motion Vectors (HOMV) for overlapping hierarchical Space-Time cubes. The Space-Time cubes selected are partially overlapped. HOMV is found to be very effective to define the motion characteristics of these cubes. We then use Bag of Features (BOF) approach to define the video as histogram of HOMV keywords, obtained using k-means clustering. The video feature, thus computed, is found to be very effective in classifying videos. We demonstrate our results with experiments on two large publicly available video database.

PUBLICATION RECORD

  • Publication year

    2013

  • Venue

    IEEE International Conference on Acoustics, Speech, and Signal Processing

  • Publication date

    2013-05-01

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1109/ICASSP.2013.6638012
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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