Video Key Frame Detection Method by Cascaded Manual Feature and Depth Feature

Key frame detection is the key link of effective video content analysis. The commonly used methods based on manual features are efficient but difficult to represent key frame features effectively, so the performance is not good. Because of the complexity of network structure, the method based on depth feature is inefficient. In sports games video, the key frame is often the last frame of shot change in the game broadcast. However, in addition to the game video, there are many other types of shots in the broadcast video, such as halftime, gradient shot and so on. So the last frame contains a lot of irrelevant content. In order to solve this problem, this paper proposes a video key frame detection method which combines manual feature and depth feature. Firstly, the last frame is obtained by shot boundary detection based on color histogram feature. Furthermore, based on histogram similarity, a similar clustering method is proposed to get candidate keyframes. Finally, the candidate keyframes are classified based on the depth neural network to get the real keyframes. The experimental results on curling match video and basketball match video show that compared with the traditional background difference method, optical flow method, etc, This method can extract key frames quickly and reliably.