Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model
This addresses scalable violence detection in surveillance footage, offering an incremental improvement by eliminating the need for attention annotations.
The paper tackled video violence detection by introducing a semi-supervised hard attention model that identifies essential video regions to remove redundant data, achieving state-of-the-art accuracies of 90.4% on RWF and 98.7% on Hockey datasets.
The significant growth of surveillance camera networks necessitates scalable AI solutions to efficiently analyze the large amount of video data produced by these networks. As a typical analysis performed on surveillance footage, video violence detection has recently received considerable attention. The majority of research has focused on improving existing methods using supervised methods, with little, if any, attention to the semi-supervised learning approaches. In this study, a reinforcement learning model is introduced that can outperform existing models through a semi-supervised approach. The main novelty of the proposed method lies in the introduction of a semi-supervised hard attention mechanism. Using hard attention, the essential regions of videos are identified and separated from the non-informative parts of the data. A model's accuracy is improved by removing redundant data and focusing on useful visual information in a higher resolution. Implementing hard attention mechanisms using semi-supervised reinforcement learning algorithms eliminates the need for attention annotations in video violence datasets, thus making them readily applicable. The proposed model utilizes a pre-trained I3D backbone to accelerate and stabilize the training process. The proposed model achieved state-of-the-art accuracy of 90.4% and 98.7% on RWF and Hockey datasets, respectively.