Transformer-based visual trackers have demonstrated significant progress owing to their
superior modeling capabilities. However, existing trackers are hampered by low speed …

Transformer-based trackers have achieved strong accuracy on the standard benchmarks.
However, their efficiency remains an obstacle to practical deployment on both GPU and …

Multiple object tracking gained a lot of interest from researchers in recent years, and it has
become one of the trending problems in computer vision, especially with the recent …

In this article, we provide an intuitive viewing to simplify the Siamese-based trackers by
converting the tracking task to a classification. Under this viewing, we perform an in-depth …

In recent years, target tracking has made great progress in accuracy. This development is
mainly attributed to powerful networks (such as transformers) and additional modules (such …

Recently, the transformer has enabled the speed-oriented trackers to approach state-of-the-
art (SOTA) performance with high-speed thanks to the smaller input size or the lighter …

The fusion of the template and search region features plays a significant role in deep
learning-based trackers. In Siamese-based trackers, different cross-correlation operations …

Convolutional Neural Networks (CNNs) and Transformer are two powerful representation
learning techniques for visual tracking. Although CNNs can effectively reduce local …

The deployment of transformers for visual object tracking has shown state-of-the-art results
on several benchmarks. However, the transformer-based models are under-utilized for …

Aerial tracking has recently shown significant potential in vision-based measurement.
Despite significant improvements, accurate occlusion tracking remains a difficult challenge …