NDLI: A compressive tracking based on time-space Kalman fusion model

Content Provider	Springer Nature Link
Author	Yun, Xiao Jing, Zhongliang Xiao, Gang Jin, Bo Zhang, Canlong
Copyright Year	2016
Abstract	The compressive tracking (CT) method is a simple yet efficient algorithm which compresses the high-dimensional features into a low-dimensional space while preserving most of the salient information. This paper proposes a compressive time-space Kalman fusion tracking algorithm to extend the CT method to the case of multi-sensor fusion tracking. Existing fusion trackers deal with multi-sensor features individually and without time-space adaptability. Besides, significant information cumulated in the updating process has not been fully exploited, which calls for a necessity for temporal information extraction. Unlike previous algorithms, the proposed fusion model is completed in both space and time domains. Also, extended Kalman filter is introduced to formulate an updating method for fusion coefficient optimization. The accuracy and robustness of the proposed fusion tracking algorithm are demonstrated by several experimental results.本文提出的时空Kalman融合模型的压缩跟踪方法将压缩跟踪算法扩展到多传感器融合跟踪的问题中。现有的融合跟踪算法忽略了多传感器特征融合的时空适用性。与现有方法不同的是,本文提出的融合模型同时在时间和空间两个领域完成。此外,本文引入扩展Kalman滤波器为融合系数提供一个优化更新方法。大量实验结果证明了该融合跟踪方法的准确性和鲁棒性。
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF
ISSN	1674733X
Journal	Science in China Series : Information Sciences
Volume Number	59
Issue Number	1
e-ISSN	18691919
Language	Chinese
Publisher	Science China Press
Publisher Date	2015-10-31
Publisher Place	Beijing
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	fusion tracking compressive tracking time-space Kalman fusion model extended Kalman filter visual tracking Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Computer Science

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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