  Existing deep Thermal InfraRed (TIR) trackers usually use the feature models
of RGB trackers for representation. However, these feature models learned on
RGB images are neither effective in representing TIR objects nor taking
fine-grained TIR information into consideration. To this end, we develop a
multi-task framework to learn the TIR-specific discriminative features and
fine-grained correlation features for TIR tracking. Specifically, we first use
an auxiliary classification network to guide the generation of TIR-specific
discriminative features for distinguishing the TIR objects belonging to
different classes. Second, we design a fine-grained aware module to capture
more subtle information for distinguishing the TIR objects belonging to the
same class. These two kinds of features complement each other and recognize TIR
objects in the levels of inter-class and intra-class respectively. These two
feature models are learned using a multi-task matching framework and are
jointly optimized on the TIR tracking task. In addition, we develop a
large-scale TIR training dataset to train the network for adapting the model to
the TIR domain. Extensive experimental results on three benchmarks show that
the proposed algorithm achieves a relative gain of 10% over the baseline and
performs favorably against the state-of-the-art methods. Codes and the proposed
TIR dataset are available at {https://github.com/QiaoLiuHit/MMNet}.
