Recent advances in computer vision and machine learning underpin a collection of
algorithms with an impressive ability to decipher the content of images. These deep learning …

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 …

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

Offline training for object tracking has recently shown great potentials in balancing tracking
accuracy and speed. However, it is still difficult to adapt an offline trained model to a target …

Discriminative correlation filters (DCFs) have\ryn been shown to perform superiorly in visual
tracking. They\ryn only need a small set of training samples from the initial frame to generate …

Tracking by siamese networks has achieved favorable performance in recent years.
However, most of existing siamese methods do not take full advantage of spatial-temporal …

Not all people are equally easy to identify: color statistics might be enough for some cases
while others might require careful reasoning about high-and low-level details. However …

Visual object tracking is a fundamental and time-critical vision task. Recent years have seen
many shallow tracking methods based on real-time pixel-based correlation filters, as well as …

Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing
to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as …

Visual object tracking is an important area in computer vision, and many tracking algorithms
have been proposed with promising results. Existing object tracking approaches can be …