Motion Influence Map for Unusual Human Activity Detection and Localization in Crowded Scenes

Dong Gyu Lee, Heung Il Suk, Sung Kee Park, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)


In this paper, we propose a novel method for unusual human activity detection in crowded scenes. Specifically, rather than detecting or segmenting humans, we devised an efficient method, called a motion influence map, for representing human activities. The key feature of the proposed motion influence map is that it effectively reflects the motion characteristics of the movement speed, movement direction, and size of the objects or subjects and their interactions within a frame sequence. Using the proposed motion influence map, we further developed a general framework in which we can detect both global and local unusual activities. Furthermore, thanks to the representational power of the proposed motion influence map, we can localize unusual activities in a simple manner. In our experiments on three public datasets, we compared the performances of the proposed method with that of other state-of-the-art methods and showed that the proposed method outperforms these competing methods.

Original languageEnglish
Article number7024902
Pages (from-to)1612-1623
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number10
Publication statusPublished - 2015 Oct

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


  • Unusual activity detection
  • crowded scenes
  • motion influence map
  • vision-based surveillance

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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