Scale prediction based MDNet for infrared target tracking

Abstract

Infrared target tracking is increasingly becoming important for various applications in recent years. However, it is still a challenging task as limited information can be obtained from the infrared image. Inspired by the excellent performance of deep tracker, a novel tracker based on MDNet is proposed. As the prior information has great value for target tracking, a modified Back-Propagation network is used for predicting the scale of target during tracking. The result of the prediction is used for generating candidate windows for online learning, which can improve the performance of tracker. To evaluate the proposed tracking algorithm, we performed experiments on the VOT-TIR2016 and AMCOM infrared data. The experimental results demonstrate that our algorithm provides a 1.94% relative gain in accuracy and 21.4% in robustness on VOT-TIR2016 when compared with MDNet.