In recent years, visual object tracking has become a very active research area. An
increasing number of tracking algorithms are being proposed each year. It is because …

The feature models used by existing Thermal InfraRed (TIR) tracking methods are usually
learned from RGB images due to the lack of a large-scale TIR image training dataset …

Thermal infrared (TIR) target tracking task is not affected by illumination changes and can be
tracked at night, on rainy days, foggy days, and other extreme weather; so it is widely used in …

Unlike the visual object tracking, thermal infrared object tracking can track a target object in
total darkness. Therefore, it has broad applications, such as in rescue and video …

Existing deep Thermal InfraRed (TIR) trackers only use semantic features to represent the
TIR object, which lack the sufficient discriminative capacity for handling distractors. This …

When dealing with complex thermal infrared (TIR) tracking scenarios, the single category
feature is not sufficient to portray the appearance of the target, which drastically affects the …

In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR)
Object Tracking Benchmark, called LSOTB-TIR, which consists of an evaluation dataset and …

Unlike visual object tracking, thermal infrared (TIR) object tracking methods can track the
target of interest in poor visibility such as rain, snow, and fog, or even in total darkness. This …

Most thermal infrared (TIR) tracking methods are discriminative, treating the tracking
problem as a classification task. However, the objective of the classifier (label prediction) is …

Abstract Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of
RGB trackers for representation. However, these feature models learned on RGB images …