Violent event detection is an interesting research problem and it is a branch of action recognition and computer vision. The detection of violent events is significant for both the public and private sectors. The automatic surveillance system is more attractive and interesting because of its wide range of applications in abnormal event detection. Since many years researchers were worked on violent activity detection and they have proposed different feature descriptors on both vision and acoustic technology. Challenges still exist due to illumination, complex background, scale changes, sudden variation, and slowmotion in videos. Consequently, violent event detection is based on the texture features of the frames in both crowded and uncrowned scenarios. Our proposed method used Local Binary Pattern (LBP) and GLCM (Gray Level Co-occurrence Matrix) as feature descriptors for the detection of a violent event. Finally, prominent features are used with five different supervised classifiers. The proposed feature extraction technique used Hockey Fight (HF) and Violent Flows (VF) two standard benchmark datasets for the experimentation.

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