Violence Detection using 3D Convolutional Neural Networks
Jiayi Su, Paris Her, Erik Clemens, Edwin E. Yaz, Susan C. Schneider, Henry Medeiros
Abstract
Accurate detection of abnormal behavior can help improve public safety. In this work, a 3D convolutional neural network (CNN) is implemented to detect violence captured by surveillance cameras. A comprehensive study of model hyper-parameter tuning is addressed to show competitive violence detection results using a general action recognition CNN without modifying the original architecture. Experimental results on three publicly available benchmark datasets show that the proposed method outperforms other sophisticated techniques designed specifically to detect violence in videos. Our analysis further indicates that reasonable network parameter adjustments can be an effective mechanism to guide the design of computer vision models in abnormal human behavior detection.