A compressive tracking based on time-space Kalman fusion model

• Published in: Science China. Information sciences Vol. 59; no. 1; pp. 117 - 131
• Main Authors: Yun, Xiao, Jing, Zhongliang, Xiao, Gang, Jin, Bo, Zhang, Canlong
• Format: Journal Article
• Language: English
• Published: Beijing Science China Press 01.01.2016; Springer Nature B.V
• Subjects: Algorithms; Computer Science; Extended Kalman filter; Information retrieval; Information Systems and Communication Service; Multisensor fusion; Research Paper; Tracking; 下线; 服务; 迁移; 压缩跟踪; fusion tracking; compressive tracking; 扩展Kalman滤波器; time-space Kalman fusion model; 012106; visual tracking; extended Kalman filter; 视觉跟踪; 融合跟踪; 时空Kalman融合模型
• ISSN: 1674-733X; 1869-1919
• DOI: 10.1007/s11432-015-5356-0

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.