Real-Time Abnormal Events Detection Combining Motion Templates and Object Localization

  • Thi-Lan LeEmail author
  • Thanh-Hai Tran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 341)


Recently, abnormal event detection has attracted great research attention because of its wide range of applications. In this paper, we propose an hybrid method combining both tracking output and motion templates. This method consists of two steps: object detection, localization and tracking and abnormal event detection. Our contributions in this paper are three-folds. Firstly, we propose a method that apply only HOG-SVM detector on extended regions detected by background subtraction. This method takes advantages of the background subtraction method (fast computation) and the HOG-SVM detector (reliable detection). Secondly, we do multiple objects tracking based on HOG descriptor. The HOG descriptor, computed in the detection phase, will be used in the phase of observation and track association. This descriptor is more robust than usual grayscale (color) histogram based descriptor. Finally, we propose a hybrid method for abnormal event detection this allows to remove several false detection cases.


Video analysis Event recognition Object detection and tracking 



The research leading to this paper was supported by the National Project B2013.01.41 “Study and develop an abnormal event recognition system based on computer vision techniques”. We would like to thank the project and people involved in this project.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.International Research Institute MICAHUST - CNRS/UMI-2954 - Grenoble INP and Hanoi University of Science and TechnologyHanoiVietnam

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