A novel violent video detection method based on improved C3D and transfer learning

Inhalt:
In recent years, due to the widespread use of surveillance systems, the crime rates significantly decreased and public security is guaranteed. However, instead of stopping ongoing crimes, these surveillance videos is often to provide clues and evidence after crimes occur. Manual monitoring is time-consuming and laborious, so it is necessary to automatically detect these violent behaviors in videos. In this paper, we propose a new violent video detection method, which improves C3D so that it can detect violent videos more effectively. In addition, we sample the input video during training, extracting one frame every two frames. In the test, the input video is not sampled. We also pre-train part of the network on the Sports-1M dataset using transfer learning method. The experimental results show that the accuracy of the method proposed in this paper reaches 99.5% on Hockey Fights, 98.37% on Violent Crowds, and 100% on Action Movie, which is very simple and effective.