Journal of Guangxi Normal University(Natural Science Edition) ›› 2018, Vol. 36 ›› Issue (4): 42-50.doi: 10.16088/j.issn.1001-6600.2018.04.006

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Infrared-Visible Target Tracking Basedon AdaBoost Confidence Map

ZHANG Canlong1,2*, SU Jiancai1,2, LI Zhixin1,2, WANG Zhiwen3   

  1. 1. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, GuilinGuangxi 541004,China;
    2.Guangxi Collaborative Innovation Center of Multi-source Information Integration andIntelligent Processing, Guilin Guangxi 541004, China;
    3. College of Computer Science and CommunicationEngineering, Guangxi University of Science and Technology, Liuzhou Guangxi 545006, China
  • Received:2018-01-01 Published:2018-10-20

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Abstract: To address the problem that the tracker is easy to drift away from the target and even failure in complex scenes, this paper presents an infrared-visible target tracking algorithm based on AdaBoost confidence map. Firstly, the target samples and background samples in infrared-visible images are characterized by using color and texture descriptor and are classified using AdaBoost classifier, and then the confidence maps of infrared and visible images are calculated based on the classification scores. Secondly, the similarity between confidence maps of target candidate and its template is calculated for visible and infrared images, and the visible similarity and infrared similarity are integrated into a joint objective function by weighting. Finally, a joint target location-shift formula is induced by performing multi-variable Taylor series expansion and maximization on the objective function, and the optimal target location is gained recursively by applying the mean shift procedure. The experimental result in infrared-visible image sequences demonstrates that the proposed method performs well in dealing with illumination change, target intersection, target occlusion and so on.

Key words: AdaBoost classifier, confidence map, infrared-visible target, fusion tracking

CLC Number: 

  • TP391.41
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[1] CAI Bing, ZHANG Canlong, LI Zhixin. Tracking Infrared-visible Target with Joint Histogram [J]. Journal of Guangxi Normal University(Natural Science Edition), 2017, 35(3): 37-44.
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