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 …

Unmanned aerial vehicles (UAVs) have recently rapidly grown to facilitate a wide range of
innovative applications that can fundamentally change the way cyber-physical systems …

The tracking-by-detection framework consists of two stages, ie, drawing samples around the
target object in the first stage and classifying each sample as the target object or as …

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 …

Abstract Discriminative Correlation Filters (DCF) have demonstrated excellent performance
for visual object tracking. The key to their success is the ability to efficiently exploit available …

Structured output support vector machine (SVM) based tracking algorithms have shown
favorable performance recently. Nonetheless, the time-consuming candidate sampling and …

In this paper we present a tracker, which is radically different from state-of-the-art trackers:
we apply no model updating, no occlusion detection, no combination of trackers, no …

We propose a novel visual tracking algorithm based on the representations from a
discriminatively trained Convolutional Neural Network (CNN). Our algorithm pretrains a …

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 …

Correlation Filter-based trackers have recently achieved excellent performance, showing
great robustness to challenging situations exhibiting motion blur and illumination changes …