Sparse codes fusion for context enhancement of night video surveillance
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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.
KeywordsVideo enhancement Sparse codes fusion (SCF) Daytime dictionary Nighttime dictionary Mutual coherence learning (MCL) Enhanced background Motion extraction
The authors would like to thank the anonymous reviewers for their helpful comments. This work is partly supported by National Science Foundation of China (Grant No. 61300092), Fundamental Research Funds for the Central Universities (Grant No. ZYGX2013J068), and Sichuan Province Science and Technology Support Program Project (Grant No. 2013GZ0151).
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