With the rapid development of artificial intelligence and intelligent manufacturing, the
traditional teaching-playback mode and the off-line programming (OLP) mode cannot meet …

The training of a feature extraction network typically requires abundant manually annotated
training samples, making this a time-consuming and costly process. Accordingly, we …

Today, artificial intelligence is everywhere in people's daily lives. Visual tracking, which is
used to identify and continuously track specific targets, is an important research domain in …

The current Siamese network based on region proposal network (RPN) has attracted great
attention in visual tracking due to its excellent accuracy and high efficiency. However, the …

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 …

Thermal infrared (TIR) pedestrian tracking is one of the important components among
numerous applications of computer vision, which has a major advantage: it can track …

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 …

Object tracking is a popular topic in the field of computer vision. The detailed spatial
information provided by a very high resolution remote sensing sensor makes it possible to …

Spatial boundary effect can significantly reduce the performance of a learned discriminative
correlation filter (DCF) model. A commonly used method to relieve this effect is to extract …