Categorizing Bi-object video activities using bag of segments and causality features

Yue Zhou, Shuicheng Yan, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the recognition problem of video activities involving two interacting moving objects under a surveillance camera. We develop a novel video activity representation scheme - 'bag of segments'. In this scheme, the video sessions are represented as a collection of independent segments, with memberships to each pre-learned visual patterns that we call codewords. To better represent the video segments with object interaction, we design a set of new features based on the prediction filter responses and the Granger Causality Test (GCT). These features capture the inter-relationship between moving objects and are combined with conventional features such as position and velocity. We validate the proposed method for the task of video activities classification with extensive experiments on a surveillance database with 867 video sessions.

Original languageEnglish (US)
Title of host publication1st ACM Workshop on Vision Networks for Behaviour Analysis, VNBA'08, Co-located with the 2008 ACM International Conference on Multimedia, MM'08
Pages55-60
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event1st ACM Workshop on Vision Networks for Behaviour Analysis, VNBA'08, Co-located with the 2008 ACM International Conference on Multimedia, MM'08 - Vancouver, BC, United States
Duration: Oct 27 2008Oct 31 2008

Publication series

NameMM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops

Other

Other1st ACM Workshop on Vision Networks for Behaviour Analysis, VNBA'08, Co-located with the 2008 ACM International Conference on Multimedia, MM'08
Country/TerritoryUnited States
CityVancouver, BC
Period10/27/0810/31/08

Keywords

  • Bag of segments
  • Classification
  • Granger Causality
  • Video activity

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

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