  Existing deep Thermal InfraRed (TIR) trackers only use semantic features to
describe the TIR object, which lack the sufficient discriminative capacity for
handling distractors. This becomes worse when the feature extraction network is
only trained on RGB images.To address this issue, we propose a multi-level
similarity model under a Siamese framework for robust TIR object tracking.
Specifically, we compute different pattern similarities on two convolutional
layers using the proposed multi-level similarity network. One of them focuses
on the global semantic similarity and the other computes the local structural
similarity of the TIR object. These two similarities complement each other and
hence enhance the discriminative capacity of the network for handling
distractors. In addition, we design a simple while effective relative entropy
based ensemble subnetwork to integrate the semantic and structural
similarities. This subnetwork can adaptive learn the weights of the semantic
and structural similarities at the training stage. To further enhance the
discriminative capacity of the tracker, we construct the first large scale TIR
video sequence dataset for training the proposed model. The proposed TIR
dataset not only benefits the training for TIR tracking but also can be applied
to numerous TIR vision tasks. Extensive experimental results on the VOT-TIR2015
and VOT-TIR2017 benchmarks demonstrate that the proposed algorithm performs
favorably against the state-of-the-art methods.
