Thermal infrared object tracking via Siamese convolutional neural networks

In this paper, we propose a novel thermal infrared (TIR) tracker via a deep Siamese convolutional neural network (CNN), named Siamesetir. Different from the most existing discriminative TIR tracking methods which treat the tracking problem as a classification problem, we treat the TIR tracking problem as a similarity verification problem. Specifically, we design a novel Siamese convolutional neural network which coalesces the multiple convolution layers to obtain richer information for tracking. Then, we train this network end to end on a large video detection dataset to learn the similarity of two arbitrary objects. Next, this pre-trained Siamese network is regarded as a similarity function simply used to evaluate the similarity between the initial target and candidates. Finally, we locate the most similar one without any adapting in the tracking process. To evaluate the performance of our TIR tracker, we conduct the experiments on the TIR tracking benchmark VOT-TIR2016. The experimental results show that the proposed method achieves very competitive performance.