Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Optimization based tracking methods have been widely successful by integrating a target
model prediction module, providing effective global reasoning by minimizing an objective …

Object detection, Person tracking, and Person property estimation (PPE) are identical
innovation areas trying to improve their accuracy in different parameters to fit various real …

We propose an online tracking algorithm that performs the object detection and data
association under a common framework, capable of linking objects after a long time span …

Recently, combining multiple features into discriminative correlation filters to improve
tracking representation has shown great potential in object tracking. Existing trackers apply …

Accurate and robust visual object tracking is one of the most challenging and fundamental
computer vision problems. It entails estimating the trajectory of the target in an image …

Recently, both correlation filters-based and Siamese-based trackers have achieved great
progress in the field of visual object tracking. Whereas, some trackers perform not that well …

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 …

Current Siamese-based trackers mainly formulate the visual tracking into two indepedent
subtasks, including classification and localization. They learn the classification subnetwork …

Recent advances in deep networks have gained great attention in infrared and visible image
fusion (IVIF). Nevertheless, most existing methods are incapable of dealing with slight …