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 …

Recently, deep learning has achieved great success in visual tracking. The goal of this
paper is to review the state-of-the-art tracking methods based on deep learning. First, we …

摘要目标跟踪一直以来都是计算机视觉领域的关键问题, 最近随着人工智能技术的飞速发展,
运动目标跟踪问题得到了越来越多的关注. 本文对主流目标跟踪算法进行了综述, 首先 …

Siamese networks have drawn great attention in visual tracking because of their balanced
accuracy and speed. However, the backbone networks used in Siamese trackers are …

Abstract Discriminative Correlation Filters (DCF) are efficient in visual tracking but suffer from
unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested to …

Observing that Semantic features learned in an image classification task and Appearance
features learned in a similarity matching task complement each other, we build a twofold …

Template-based discriminative trackers are currently the dominant tracking paradigm due to
their robustness, but are restricted to bounding box tracking and a limited range of …

Abstract In recent years, Discriminative Correlation Filter (DCF) based methods have
significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever …

In the field of generic object tracking numerous attempts have been made to exploit deep
features. Despite all expectations, deep trackers are yet to reach an outstanding level of …

In recent years, many tracking algorithms achieve impressive performance via fusing
multiple types of features, however, most of them fail to fully explore the context among the …