Sparse codes fusion for context enhancement of night video surveillance

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• 作者：Xianshu Ding ; Hang Lei ; Yunbo Rao
• 关键词：Video enhancement ; Sparse codes fusion (SCF) ; Daytime dictionary ; Nighttime dictionary ; Mutual coherence learning (MCL) ; Enhanced background ; Motion extraction
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：75
• 期：18
• 页码：11221-11239
• 全文大小：2,252 KB
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721
• 卷排序：75

文摘

Fusion-based method for video enhancement has been playing a basic but significant role, which is also proved high-efficiency. Still, there are some open questions, such as lamp-off problem, over-enhanced moving objects and night shadow. To resolve the problems, a novel method—sparse codes fusion (SCF) is proposed. With plenty of samples from daytime videos and nighttime videos of the same scene, we learn and obtain a daytime dictionary and a nighttime dictionary using the proposed mutual coherence learning (MCL) algorithm. These two dictionaries are utilized for fusion and extracting context enhanced background. Moreover, we reconstruct the nighttime dictionary to get nighttime background that would be applied in motion extraction. Then the moving objects are added into the enhanced background. Extensive experimental results show a highly comprehensive description of video frames that leads to improvements over the state of the art on many usual public video datasets.