Papers › Robust Real-Time Violence Detection in Video Using CNN And LSTM
Robust Real-Time Violence Detection in Video Using CNN And LSTM
almamon rasool abdali, Rana F. Al-Tuma
Detection of a violence event in surveillance systems is playing a significant role in law enforcement and city safety. The effectiveness of violence event detectors measures by the speed of response and the accuracy and the generality over different kind of video sources with a different format. Several studies worked on the violence detection with focus either on speed or accuracy or both but not taking into account the generality over different kind of video sources. In this paper, we proposed a real-time violence detector based on deep-learning methods. The proposed model consists of CNN as a spatial feature extractor and LSTM as temporal relation learning method with a focus on the three-factor (overall generality - accuracy - fast response time). The suggested model achieved 98% accuracy with speed of 131 frames/sec. Comparison of the accuracy and the speed of the proposed model with previous works illustrated that the proposed model provides the highest accuracy and the fastest speed among all the previous works in the field of violence detection.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Classification | Hockey Fight Detection Dataset | CNN+LSTM | 1:1 Accuracy | 98% | #1 of 2 | Archive leaderboard | report |
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