Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Visual object tracking has attracted widespread interests recently. Due to the complementary
features provided by visible and infrared images, fusion tracking based on visible and …

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 …

As one of the most important defects of concrete, cracks seriously threaten the service life
and safety of concrete structures, and various safety incidents caused by the collapse of …

Different from visible cameras which record intensity images frame by frame, the biologically
inspired event camera produces a stream of asynchronous and sparse events with much …

Thermal infrared (TIR) target tracking is susceptible to occlusion and similarity interference,
which obviously affects the tracking results. To resolve this problem, we develop an Aligned …

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 …

Object tracking belongs to active research areas in computer vision. We are interested in
matching-based trackers exploiting deep machine learning known as Siamese trackers …

We address the problem of multi-modal object tracking in video and explore various options
available for fusing the complementary information conveyed by the visible (RGB) and …