Abstract
Scene surveillance video is a kind of video which are captured by stationary camera for a long time in specific surveillance scene. Due to regular movement of vehicles with similarity structures, models and appearances, surveillance video produce amounts of redundancy and needs to be efficiently coded for transmission and storage. In this study, we investigated the video redundancy generation mechanism of scene surveillance, exploit and presents a new redundancy type-Global Object Redundancy (GOR), it is proven that the vehicles occupy the mostly proportion which caused by amounts of vehicles movement. Secondly, aiming at global vehicle objects representation and GOR elimination, a global object representation scheme of scene surveillance video based on model and feature parameters is introduced, by establish a global knowledge dictionary and feature parameter sets, low bitrate with high quality compression can be achieved due to only few vehicle objects individual semantic and feature parametric be transfer and coded. Finally, we carried out preliminary experiments in simulation environment and shows that the object representation scheme can effectively improve the compression of long-term archive surveillance video which with a certain of image quality assurance.
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Acknowledgments
The National Nature Science Foundation of China (No. 61231015), National High Technology Research and Development Program of China (863 Program) No. 2015AA016306, EU FP7 QUICK project under Grant Agreement No. PIRSES-GA-2013-612652, China Postdoctoral Science Foundation funded project (2013M530350, 2014M562058), Fundamental Research Funds for the Central Universities (2042014kf0025), Internet of Things Development Funding Project of Ministry of industry in 2013 (No. 25).
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Ma, M., Hu, R., Chen, S., Xiao, J., Wang, Z., Qu, S. (2015). Global Object Representation of Scene Surveillance Video Based on Model and Feature Parameters. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_22
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DOI: https://doi.org/10.1007/978-3-319-24075-6_22
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