Bad video classification based on deep learning
Yu Dai, Jun Lang · 2023
In modern society, violence and pornography often occur in KTV entertainment venues and on campus. It is of great significance to protect students' physical and mental health and maintain the order of KTV entertainment venues if violent and pornographic clips can be instantly identified from surveillance videos. However, pornographic video identification networks in general are different from violent video identification networks. Moreover, the traditional video classification network is not suitable for the classification of bad videos. Therefore, we propose a new deep learning network architecture for bad video classification. The network consists of a primary branch that uses optical flow information and a secondary branch that uses keyframe information. Experimental results show that this network structure is more accurate than the existing related video classification networks.