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Target tracking based on non-linear kernel density estimation and Kalman filter
Wu Y(吴阳); Zhou XF(周晓锋); Zhang YC(张宜弛)
作者部门数字工厂研究室
会议名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
会议日期June 8-12, 2015
会议地点Shenyang, China
会议录名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
出版者IEEE
出版地Piscataway, NJ, USA
2015
页码462-466
收录类别EI ; CPCI(ISTP)
EI收录号20161402187611
WOS记录号WOS:000380502300089
产权排序2
ISSN号2379-7711
ISBN号978-1-4799-8730-6
关键词Target Tracking Non-linear Kernel Density Estimation Mean Shift Kalman Filter
摘要This paper chooses Mean Shift algorithm to track target based on non-linear kernel density estimation and Kalman filter. Kernel density estimation is a probability density estimation method, which is used to detect moving target and update the target color histogram. The interest targets are obtained by labeling connected region in the detected binary image. Kalman filtering is employed to predict the position of the target being tracked, giving a starting searching window for Mean Shift tracking. Experimental results show that the method proposed is effective and fast in implementation, which satisfies the real-time requirement, it is capable of handling occlusion problem, meanwhile it is robust against the effects of unstable scene illumination.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/18529
专题数字工厂研究室
作者单位1.Wuxi CAS Ubiquitous Information Technology RandD, Center Co., Ltd, Wuxi, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
推荐引用方式
GB/T 7714
Wu Y,Zhou XF,Zhang YC. Target tracking based on non-linear kernel density estimation and Kalman filter[C]. Piscataway, NJ, USA:IEEE,2015:462-466.
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