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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)
Department数字工厂研究室
Conference Name2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
Conference DateJune 8-12, 2015
Conference PlaceShenyang, China
Source Publication2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages462-466
Indexed ByEI ; CPCI(ISTP)
EI Accession number20161402187611
WOS IDWOS:000380502300089
Contribution Rank2
ISSN2379-7711
ISBN978-1-4799-8730-6
KeywordTarget Tracking Non-linear Kernel Density Estimation Mean Shift Kalman Filter
AbstractThis 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.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/18529
Collection数字工厂研究室
Affiliation1.Wuxi CAS Ubiquitous Information Technology RandD, Center Co., Ltd, Wuxi, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
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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