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fusion tracking in color and infrared images using joint sparse representation

Science China(Information Sciences) 2012年第3期55卷 590-599页

作者： LIU HuaPing1,2,3 & SUN FuChun1,2,3 1Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China 2State Key Laboratory of Intelligent Technology and Systems, Beijing 100084, China 3Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China Department of Computer Science and Technology Tsinghua University Beijing China State Key Laboratory of Intelligent Technology and Systems Beijing China Tsinghua National Laboratory for Information Science and Technology Beijing China

Currently sparse signal reconstruction gains considerable interest and is applied in many fields. In this paper, a similarity induced by joint sparse representation is designed to construct the likelihood function of particle filter tracker so that the color visual spectrum and thermal spectrum images can be fused for object tracking. The proposed fusion scheme performs joint sparse representation calculation on both modalities and the resultant tracking results are fused using min operation on the sparse representation coefficients. In addition, a co-learning approach is proposed to update the reference templates of both modality and enhance the tracking robustness. The proposed fusion scheme outperforms state-of-the-art approaches, and its effectiveness is verified using OTCBVS database.

关键词： joint sparse representation fusion tracking color image infrared image

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A compressive tracking based on time-space Kalman fusion model

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Science China(Information Sciences) 2016年第1期59卷 120-134页

作者： Xiao YUN Zhongliang JING Gang XIAO Bo JIN Canlong ZHANG School of Aeronautics and Astronautics Shanghai Jiao Tong University School of Software Engineering East China Normal University School of Computer Science and Information Technology Guangxi Normal University

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.

关键词： fusion tracking compressive tracking time-space Kalman fusion model extended Kalman filter visual tracking

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A multi-cue mean-shift target tracking approach based on fuzzified region dynamic image fusion

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Science China(Information Sciences) 2012年第3期55卷 577-589页

作者： XIAO Gang1 , YUN Xiao1 & WU JianMin2 1School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China 2Science and Technology on Avionics Integration Laboratory, Shanghai 200233, China School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China Science and Technology on Avionics Integration Laboratory Shanghai China

Traditional target tracking algorithms based on single sensor images are unstable and have low accuracy. Based on regional target detection and fuzzy region rules, a fuzzy region-based multi-sensor image fusion approach is proposed in this paper. The similarity measure weight is adapted to this dynamic image fusion algorithm, while the tracking method uses the proposed multi-cue mean-shift tracking algorithm. Three experimental results using real world image sequences are evaluated using the steady state square root mean error. The fusion and tracking experiments indicate that the proposed approach is effective and efficient when aiming at a target moving from one area to a different area, which meets the robustness and real-time requirements.

关键词： fusion tracking fuzzy region dynamic image fusion target tracking multi-cue mean-shift

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