In recent years, visual object tracking has become a very active research area. An
increasing number of tracking algorithms are being proposed each year. It is because …

Single-object tracking is regarded as a challenging task in computer vision, especially in
complex spatio-temporal contexts. The changes in the environment and object deformation …

Siamese approaches address the visual tracking problem by extracting an appearance
template from the current frame, which is used to localize the target in the next frame. In …

The robustness of deep neural networks is frequently compromised when faced with
adversarial examples, common corruptions, and distribution shifts, posing a significant …

Although the correlation filter-based trackers achieve the competitive results both on
accuracy and robustness, there is still a need to improve the overall tracking capability. In …

The core component of most modern trackers is a discriminative classifier, tasked with
distinguishing between the target and the surrounding environment. To cope with natural …

We propose a new Group Feature Selection method for Discriminative Correlation Filters
(GFS-DCF) based visual object tracking. The key innovation of the proposed method is to …

Recent years have seen greater interest in the use of discriminative classifiers in tracking
systems, owing to their success in object detection. They are trained online with samples …

In this paper, we present a simple yet fast and robust algorithm which exploits the dense
spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal …

Although not commonly used, correlation filters can track complex objects through rotations,
occlusions and other distractions at over 20 times the rate of current state-of-the-art …