• Tejas Pethkar
• Megha Pallewar
• Lakhan Musale
• Amit Anand

Abnormal activity will lead to the uncommon changes in the crowd behavior. In other words, the crowd motion changes conforms to certain rules for valid behaviors, while for abnormal events the motion changes are uncontrolled. The motion-changed rules to detect and localize abnormal behavior in crowd videos. Specifically, we first generate the motion patterns based on the descriptor of collectiveness. Then each frame pair is represented as a transfer matrix whose elements are the difference of a set of motion patterns. Thereafter, the motion-changed rules are constructed in the transformation space using bag-of-words approach. Finally, the proposed approach measures the similarity between motion changed rules and the incoming video data in order to examine whether the actions are anomalous. The approach is tested on the challenging datasets of crowd videos taken . The experimental results demonstrate the effectiveness of the proposed method for detection abnormal behavior.

Abnormal activity Detection , SVM , KNN, Logistic Regression, Motion Change Rule .

"Abnormal Activity Detection ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.541-548, May-2023, Available :http://www.jetir.org/papers/JETIRFX06094.pdf

"Abnormal Activity Detection ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pp541-548, May-2023, Available at : http://www.jetir.org/papers/JETIRFX06094.pdf