ISurveillance: intelligent framework for multiple events detection in surveillance videos

Mei Kuan Lim, Szeling Tang, Chee Seng Chan

Research output: Contribution to journalArticleResearchpeer-review

42 Citations (Scopus)


Research in the video surveillance is gaining more popularity due to its widespread applications as well as social impact. In this paper, we present an intelligent framework for detection of multiple events in surveillance videos. Based on the principle of compositionality, we modularize the surveillance problems into a set of variables comprising regions-of-interest, classes (i.e. human, vehicle), attributes (i.e. speed, locality) and a set of notions (i.e. rules) associated to each of the attributes to construct a knowledge-based understanding of the environment. The final output from the reasoning process, which combines the definition domains of the various variables, allows a broader and integrated understanding of complex pattern of activities in the scene. This is in contrast to the state-of-the-art solutions that are only able to perform only a singular task, at a time. Experimental results on both the public and real-time datasets have demonstrated the effectiveness and robustness of the proposed framework in detecting multiple events in surveillance videos.

Original languageEnglish
Pages (from-to)4704-4715
Number of pages12
JournalExpert Systems with Applications
Issue number10
Publication statusPublished - Aug 2014
Externally publishedYes


  • Compositional modeling
  • Multiple events detection
  • Video surveillance

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