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	Improving motion state change object detection by using block background context
	Lin, Dazhen ; Cao, Donglin ; Zeng, Hualin ; Lin DZ(林达真) ; Cao DL(曹冬林) ; Ceng HL(曾华琳)
	2014
关键词	Artificial intelligence Learning algorithms Object recognition Semantics
英文摘要	Conference Name:2014 14th UK Workshop on Computational Intelligence, UKCI 2014. Conference Address: Bradford, West Yorkshire, United kingdom. Time:September 8, 2014 - September 10, 2014.; Motion state change object detection, such as stopped objects detection, is one of important topics in Video Surveillance Systems. Generally, backgrounds in the most Video Surveillance Systems have the property of pureness and self-similarity. In this paper, we propose a block background context based background model to solve the motion state change problem. Unlike the classical background model, our approach first models blocks of background, and then determines the learning rate of each block background model by using the block background context information. There are two main advantages. First, the model adaptively selects the learning rate for each block of background model, and that is more flexible than the adaptive learning rate for the whole background. Second, context information helps the determination of true foreground and brings in more reliable information in foreground detection. Our experiments results show that our model outperforms the higher and lower learning rate Gaussian mixture background model in motion state change object detection.
语种	英语
出处	http://dx.doi.org/10.1109/UKCI.2014.6930187
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86848]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Lin, Dazhen,Cao, Donglin,Zeng, Hualin,et al. Improving motion state change object detection by using block background context. 2014-01-01.

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