A review on state-of-the-art violence detection techniques

Muhammad Ramzan, Adnan Abid, Hikmat Ullah Khan*, Shahid Mahmood Awan, Amina Ismail, Muzamil Ahmed, Mahwish Ilyas, Ahsan Mahmood

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)
3 Downloads (Pure)


With the rapid growth of surveillance cameras to monitor the human activity demands such system which recognize the violence and suspicious events automatically. Abnormal and violence action detection has become an active research area of computer vision and image processing to attract new researchers. The relevant literature presents different techniques for detection of such activities from the video proposed in the recent years. This paper reviews various state-of-the-art techniques of violence detection. In this paper, the methods of detection are divided into three categories that is based on classification techniques used: violence detection using traditional machine learning, using support vector machine (SVM), and using deep learning. The feature extraction techniques and object detection techniques of the each single method are also presented. Moreover, datasets and video features that used in the techniques, which play a vital role in recognition process are also discussed. For better understanding, the steps of the research approaches have been presented in an architecture diagram. The overall research findings have been discussed which may be helpful for finding the potential future work in this research domain.
Original languageEnglish
Pages (from-to)107560-107575
Number of pages16
JournalIEEE Access
Publication statusPublished - 30 Jul 2019
Externally publishedYes


  • violence detection
  • violent behavior
  • support vector machine
  • deep learning
  • machine learning
  • surveillance camera
  • computer vision


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