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 …

As autonomous vehicles become more common on the roads, their advancement draws on
safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper …

The perception of drones, also known as Unmanned Aerial Vehicles (UAVs), particularly in
infrared videos, is crucial for effective anti-UAV tasks. However, existing datasets for UAV …

We introduce here a large tracking database that offers an unprecedentedly wide coverage
of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon …

Tracking by natural language specification is a new rising research topic that aims at
locating the target object in the video sequence based on its language description …

Deep convolutional neural networks (CNNs) have been popularly adopted in image super-
resolution (SR). However, deep CNNs for SR often suffer from the instability of training …

Recently, convolutional neural networks (CNNs) have brought impressive improvements for
object detection. However, detecting targets in infrared images still remains challenging …

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 …

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 …

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 …