Server node video processing based on feature depth analysis algorithm

Abstract

ABSTRACT The complex and diverse server video data leads to the problem of effective retrieval of these data. The current shot edge detection algorithm and key frame extraction algorithm in server node video processing have problems such as poor extraction performance and poor adaptability. Therefore, the research combined the feature depth analysis to improve the two, and the performance was verified by experiments. The shot detection algorithm is verified by modifying the secondary detection model. This method can detect lens mutation, gradual change and other phenomena well, and the accuracy rate can reach 99.7%. The precision under the gradient lens is 92.08%, far higher than 63.50% and 85.39% of ISIFT and CS-DFS. In the verification experiment using Convolution Neural Networks (CNNs) key frame extraction algorithm, the number of key frame extractions of the proposed algorithm can reach up to 88 frames. Compared with other methods, the accuracy of the algorithm studied can reach 99.67%, which is higher than the comparison algorithm. In general, the improved algorithm proposed in the study has high adaptability to edge detection and the ability to express key frame video, and has high practicability in actual server node video processing.