• Printed Journal
  • Indexed Journal
  • Peer Reviewed Journal
Journal of Applied Science & Engineering

Dhaka University Journal of Applied Science & Engineering

Issue: Vol. 7, No. 1, January 2022
Title: Violent Human Behavior Detection from Videos using Machine Learning
  • Mohammad Sadat Hussain Rafsanjani
    Institute of Information Technology, University of Dhaka, Dhaka-1000
  • Ahmedul Kabir
    Institute of Information Technology, University of Dhaka, Dhaka-1000
Keywords: Computer Vision, Machine Learning, Violent Human Behavior, Violent Behavior Detection.

Surveillance and security cameras are becoming much more common in city streets, shopping malls, private homes and many other places. In recent years terrorist attacks in shopping malls, schools and public places have increased. Such places, if equipped with a computer vision-based system which can effectively identify abnormal or violent human behavior, can help us in saving many human lives in due time. In this paper, we present such a system which can detect abnormal or violent human behavior in a video using machine learning. The system first identifies every human present in a video, extracts some vital data for that person and then, based on those data points, trains machine learning models to perform the classification. We have found that the system works reasonably well under certain conditions.

  1. K. Park, Y. Lin, V. Metsis, Z. Le, F.S. Makedon, ”Abnormal Human Behavioral Pattern Detection in Assisted Living Environments”, PETRA ’10: Proceedings of the3rd International Conference on Pervasive Technologies Related to Assistive Environments Article No.: 9 Pages 1–8, 2010, DOI: https://doi.org/10.1145/1839294.1839305
  2. X. Wu, Y. Ou, H. Qian, Y. Xu, ”A Detection System for Human Abnormal Behavior”, IEEE RSJ International Conference on Intelligent Robots and Systems, 2005, DOI:10.1109/IROS.2005.1545205
  3. P. Afsar, P. Cortez, H. Santos, ”Automatic visual detection of human behavior”, Santos, Expert Systems with Applications,Volume 42, Issue 20, Pages 6935-6956, 2015
  4. B. Krausz, C. Bauckhage, ”Automatic Detection of Dangerous Motion Behavior in Human Crowds”, 8th IEEE InternationalConference on Advanced Video and Signal Based Surveillance (AVSS),2011, DOI: 10.1109/AVSS.2011.6027326
  5. S. R. Musse, D. Thalmann,”A Model of Human Crowd Behavior: GroupInter-Relationship and Collision Detection Analysis”, Inter-Relationshipand Collision Detection Analysis. Computer Animation and Simulation’97. Eurographics. Springer, Vienna, 1997
  6. M. J. Roshtkhari, M. D. Levine, ”Online Dominant andAnomalous Behavior Detection in Videos”, IEEE Conference on Computer Vision and Pattern Recognition, 2013
  7. J.R. Quinlan, ”Simplifying Decision Trees”, International Journal ofMan-Machine Studies, Volume 27, Issue 3, Pages 221-234, 1987
  8. C. Jin, L. De-lin, M. Fen-xiang, ”An Improved ID3 DecisionTree Algorithm”, 4th International Conference on Computer Science& Education, 2009
  9. O. L. Mangasarian, D. R. Musicant, ”Active Support Vector Machine Classification”, Advances in neural information processingsystems, 2000
  10. A. Tzotsos, D. Argialas, ”A Support Vector Machine Approach for Object Based Image Analysis”, 1st International Conference on Objectbased Image Analysis, 2008
  11. V. Garcia, E. Debreuve, M. Barlaud, ”Fast k Nearest Neighbor Search using GPU”, 2008, arXiv:0804.144824
  12. J. M. Keller, M. R. Gray, J. A. Givens, ”A Fuzzy KNearest Neighbor Algorithm”, IEEE Transactions on Systems, Man, and Cybernetics, Volume: SMC-15 , Issue: 4, 1985
  13. C. J. Peng , K. L. Lee, G. M. Ingersoll, ”An Introduction to Logistic Regression Analysis and Reporting”, The Journalof Educational Research Volume 96, - Issue 1, 2002
  14. I. Rish ”An empirical study of the naive Bayes classifier”, abnormal behavior, IJCAI Work Empirical Methods Artif Intell, 2001
  15. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, ”YouOnly Look Once: Unified, Real-Time Object Detection”, 2015,arXiv:1506.02640