Deep learning (DL) has made breakthroughs in many computer vision tasks and also in
visual tracking. From the beginning of the research on the automatic acquisition of high …

The nascent applicability of multi-camera tracking (MCT) in numerous real-world
applications makes it a significant computer vision problem. While visual tracking of objects …

In this paper, we present a new sequence-to-sequence learning framework for visual
tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem …

Tracking often uses a multi-stage pipeline of feature extraction, target information
integration, and bounding box estimation. To simplify this pipeline and unify the process of …

The current popular two-stream, two-stage tracking framework extracts the template and the
search region features separately and then performs relation modeling, thus the extracted …

Transformer trackers have achieved impressive advancements recently, where the attention
mechanism plays an important role. However, the independent correlation computation in …

Optimization based tracking methods have been widely successful by integrating a target
model prediction module, providing effective global reasoning by minimizing an objective …

It is a challenging task to learn discriminative representation from images and videos, due to
large local redundancy and complex global dependency in these visual data. Convolution …

Recently Transformer has been largely explored in tracking and shown state-of-the-art
(SOTA) performance. However, existing efforts mainly focus on fusing and enhancing …

We present ARTrack, an autoregressive framework for visual object tracking. ARTrack
tackles tracking as a coordinate sequence interpretation task that estimates object …