Abstract
Typical manually-selected features are insufficient to reliably detect violence actions. In this paper, we present a violence detection model that is based on a bi-channels convolutional neural network (CNN) and the support vector machine (SVM). The major contributions are twofolds: (1) we fork the original frames and the differential images into the proposed bi-channels CNN to obtain the appearance features and the motion features respectively. (2) The linear SVMs are adopted to classify the features and a label fusion approach is proposed to improve detection performance by integrating the appearance and motion information. We compared the proposed model with several state-of-the-art methods on two datasets. The results are promising and the proposed method can achieve real-time performance of 30 fps.
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Xia, Q., Zhang, P., Wang, J., Tian, M., Fei, C. (2018). Real Time Violence Detection Based on Deep Spatio-Temporal Features. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_17
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