Semisupervised Surveillance Video Character Extraction and Recognition With Attentional Learning Multiframe Fusion

Character extraction in the video is very helpful to the understanding of the video content, especially the artificially superimposed characters such as time and place in the surveillance video. However, the performance of the existing algorithms does not meet the needs of application. Therefore, the authors improve semisupervised surveillance video character extraction and recognition with attentional learning multiframe feature fusion. First, the multiframe fusion strategy based on an attention mechanism is adopted to solve the target missing problem, and the Dense ASPP network is introduced to solve the character multiscale problem. Second, a character image denoising algorithm based on semisupervised fuzzy C-means clustering is proposed to isolate and extract clean binary character images. Finally, for some video characters that may involve privacy, traditional and deep learning-based video restoration algorithms are used for characteristic elimination.