Scene-aware classifier and re-detector for thermal infrared tracking

Compared with common visible light scenes, the target of infrared scenes lacks information such as the color, texture. Infrared images have low contrast, which not only lead to interference between targets, but also interference between the target and the background. In addition, most infrared tracking algorithms lack a redetection mechanism after lost target, resulting in poor tracking effect after occlusion or blurring. To solve these problems, we propose a scene-aware classifier to dynamically adjust low, middle, and high level features, improving the ability to utilize features in different infrared scenes. Besides, we designed an infrared target re-detector based on multi-domain convolutional network to learn from the tracked target samples and background samples, improving the ability to identify the differences between the target and the background. The experimental results on VOT-TIR2015, VOT-TIR2017 and LSOTB-TIR show that the proposed algorithm achieves the most advanced results in the three infrared object tracking benchmark.