Infrared and Visible Fusion for Robust Object Tracking via Local Discrimination Analysis

The infrared images generally contain less information but the visible images are easily affected by environments.To address this problem,a local discrimination analysis based infrared and visible multi-source information cooperative tracking approach is presented in this paper.From the view of evaluating the image information’s ability of distinguishing the object form background,the Fisher linear discrimination theory is introduced to design the discriminative function between the target and background in local regions.Based on this,the fusion of multi-source image is executed adaptively on the feature level.Finally,we incorporate the proposed fusion method into the particle filter tracking framework to achieve the object tracking.Experimental results demonstrates that, compared with the tracking system with single image source,the proposed algorithm can effectively fuse the infrared and visible images to reliably track the object.