A Video Shot Boundary Detection Approach Based on CNN Feature

In nowadays, as the development of digital photographic technology, video files grow rapidly, there is a great demand for automatic video semantic analysis in many scenes, such as video semantic understanding, content-based analysis, video retrieval. Shot boundary detection is a key basic technology and first step for video analysis. However, recent methods are time consuming and performs bad in the gradual transition detection. In this paper we proposed a new approach which used CNN model to extract features of video sequence parallelly based on GPU, so we can simplify the expression of video and reduce the calculation time for shot detection, and took local frame similarity and dual-threshold sliding window similarity into consideration to increase recall and precise of shot detection. The experimental result shows that the proposed method can achieve a high F1 score and excellent detection speed.