Implementing Low Level Features for Human Aggressive Movement Detection

  • Tuan Khalisah Tan Zizi
  • Suzaimah RamliEmail author
  • Norazlin Ibrahim
  • Norulzahrah Mohd Zainudin
  • Lili Nurliyana Abdullah
  • Nor Asiakin Hasbullah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9429)


In this real world, being able to identify the signs of imminent abnormal behaviors such as aggression or violence and also fights, is of extreme importance in keeping safe those in harm’s way. This research propose an approach to figure out human aggressive movements using Horn-Schunck optical flow algorithm in order to find the flow vector for all video frames. The video frames are collected using digital camera. This research guides and discovers the patterns of body distracted movement so that suspect of aggression can be investigated without body contact. Using the vector of this method, the abnormal and normal video frames are then classified and utilized to define the aggressiveness of humans. Preliminary experiment result showed that the low level of feature extraction can classify human aggressive and non-aggressive movements.


Optical flow Horn-Schunck algorithm Aggressive movement Non-aggressive movement 



The first author would like to express an appreciation and acknowledge the financial contributions from Ministry of Higher Education for their awarded grant RACE (RACE/F3/SG5/UPNM/3), January 2015--January 2017


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tuan Khalisah Tan Zizi
    • 1
  • Suzaimah Ramli
    • 1
    Email author
  • Norazlin Ibrahim
    • 2
  • Norulzahrah Mohd Zainudin
    • 1
  • Lili Nurliyana Abdullah
    • 3
  • Nor Asiakin Hasbullah
    • 1
  1. 1.Universiti Pertahanan Nasional MalaysiaKuala LumpurMalaysia
  2. 2.Universiti Kuala Lumpur Malaysia France InstituteBandar Baru BangiMalaysia
  3. 3.Universiti Putra MalaysiaSerdangMalaysia

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