Adaptive KCF learning with fused infrared information
Hao Yang, Yingqing Huang, Zhihong Xie · 2018
In the land battlefield environment, the tracking of personnel target is mainly affected by the complex environment, like smog, rain, shadows and partial shelter from the woods. These elements make the effect of kernelized correlation filters (KCF) target tracking based on visible light very unsatisfactory, because the brightness, color and rich texture information mainly included in visible light imaging are polluted. The infrared imaging system, due to its sensitivity to heat source, can be perceived in the dark and has low dependence on the surrounding environment. However, limited by its own characteristics, the single infrared imaging system loses some visible light information, such as light intensity, texture and color, and the image resolution is low. Considering the characteristics of visible light and infrared imaging are complementary, it’s reasonable to fuse integral channel features (ICF) of infrared gray image into HOG features of visible light images, and adjust model update rate corresponding to the degree of occlusion of target in the infrared image, to achieve more robust tracking effect.