Skip to main content


  • Research Article
  • Open Access

A Macro-Observation Scheme for Abnormal Event Detection in Daily-Life Video Sequences

EURASIP Journal on Advances in Signal Processing20102010:525026

  • Received: 19 October 2009
  • Accepted: 8 April 2010
  • Published:


We propose a macro-observation scheme for abnormal event detection in daily life. The proposed macro-observation representation records the time-space energy of motions of all moving objects in a scene without segmenting individual object parts. The energy history of each pixel in the scene is instantly updated with exponential weights without explicitly specifying the duration of each activity. Since possible activities in daily life are numerous and distinct from each other and not all abnormal events can be foreseen, images from a video sequence that spans sufficient repetition of normal day-to-day activities are first randomly sampled. A constrained clustering model is proposed to partition the sampled images into groups. The new observed event that has distinct distance from any of the cluster centroids is then classified as an anomaly. The proposed method has been evaluated in daily work of a laboratory and BEHAVE benchmark dataset. The experimental results reveal that it can well detect abnormal events such as burglary and fighting as long as they last for a sufficient duration of time. The proposed method can be used as a support system for the scene that requires full time monitoring personnel.


  • Quantum Information
  • Video Sequence
  • Cluster Model
  • Full Article
  • Benchmark Dataset

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

Department of Industrial Engineering and Management, Yuan-Ze University, 135 Yuan-Tung Road, Nei-Li, Tao-Yuan, 32026, Taiwan


© W.-Y. Chiu and D.-M. Tsai. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.