  Compared with visible object tracking, thermal infrared (TIR) object tracking
can track an arbitrary target in total darkness since it cannot be influenced
by illumination variations. However, there are many unwanted attributes that
constrain the potentials of TIR tracking, such as the absence of visual color
patterns and low resolutions. Recently, structured output support vector
machine (SOSVM) and discriminative correlation filter (DCF) have been
successfully applied to visible object tracking, respectively. Motivated by
these, in this paper, we propose a large margin structured convolution operator
(LMSCO) to achieve efficient TIR object tracking. To improve the tracking
performance, we employ the spatial regularization and implicit interpolation to
obtain continuous deep feature maps, including deep appearance features and
deep motion features, of the TIR targets. Finally, a collaborative optimization
strategy is exploited to significantly update the operators. Our approach not
only inherits the advantage of the strong discriminative capability of SOSVM
but also achieves accurate and robust tracking with higher-dimensional features
and more dense samples. To the best of our knowledge, we are the first to
incorporate the advantages of DCF and SOSVM for TIR object tracking.
Comprehensive evaluations on two thermal infrared tracking benchmarks, i.e.
VOT-TIR2015 and VOT-TIR2016, clearly demonstrate that our LMSCO tracker
achieves impressive results and outperforms most state-of-the-art trackers in
terms of accuracy and robustness with sufficient frame rate.
