Visual target tracking is one of the most sought-after yet challenging research topics in
computer vision. Given the ill-posed nature of the problem and its popularity in a broad …

Deep learning-based methods monopolize the latest research in the field of thermal infrared
(TIR) object tracking. However, relying solely on deep learning models to obtain better …

Visual tracking is one of the most fundamental topics in computer vision. Numerous tracking
approaches based on discriminative correlation filters or Siamese convolutional networks …

Although numerous recent tracking approaches have made tremendous advances in the
last decade, achieving high-performance visual tracking remains a challenge. In this paper …

In this paper, we present a unified spatio-temporal attention MixFormer framework for visual
object tracking. Within the vision transformer framework, we design a cohesive network …

Visual tracking is one of the most challenging computer vision problems. In order to achieve
high performance visual tracking in various negative scenarios, a novel cascaded Siamese …

Visual object tracking, in which the spatial location of a target object is tracked across
different camera perspectives, is an important task in computer vision. Deep neural …

Visual object tracking, in which the spatial location of a target object is tracked across
different camera perspectives, is an important task in computer vision. Deep neural …

In this paper, we present a unified spatio-temporal attention MixFormer framework for visual
object tracking. Within the vision transformer framework, we design a cohesive network …

Due to the real-time tracking and location accuracy of the kernel correlation filtering (KCF)
algorithm, this method is now widely used in object tracking tasks. However, whether KCF or …