Visible to the public Block RLS algorithm for surveillance video processing based on image sparse representation

TitleBlock RLS algorithm for surveillance video processing based on image sparse representation
Publication TypeConference Paper
Year of Publication2017
AuthorsBao, D., Yang, F., Jiang, Q., Li, S., He, X.
Conference Name2017 29th Chinese Control And Decision Conference (CCDC)
Date Publishedmay
Keywordsbackground subtraction, block recursive least square, block recursive least square algorithm, BRLS algorithm, compressed sensing, compressed sensing system, computational complexity, Dictionaries, dictionary learning, Human Behavior, image processing, Image reconstruction, image representation, image sparse representation, Least squares approximations, pubcrawl, Resiliency, Scalability, surveillance, Surveillance video, surveillance video processing, Training, Transforms, video surveillance

Block recursive least square (BRLS) algorithm for dictionary learning in compressed sensing system is developed for surveillance video processing. The new method uses image blocks directly and iteratively to train dictionaries via BRLS algorithm, which is different from classical methods that require to transform blocks to columns first and then giving all training blocks at one time. Since the background in surveillance video is almost fixed, the residual of foreground can be represented sparsely and reconstructed with background subtraction directly. The new method and framework are applied in real image and surveillance video processing. Simulation results show that the new method achieves better representation performance than classical ones in both image and surveillance video.

Citation Keybao_block_2017