Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

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 …

Visual tracking is fundamentally the problem of regressing the state of the target in each
video frame. While significant progress has been achieved, trackers are still prone to failures …

Abstract We present Siam R-CNN, a Siamese re-detection architecture which unleashes the
full power of two-stage object detection approaches for visual object tracking. We combine …

Most existing trackers based on discriminative correlation filters (DCF) try to introduce
predefined regularization term to improve the learning of target objects, eg, by suppressing …

Abstract The Visual Object Tracking challenge VOT2019 is the seventh annual tracker
benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; …

In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object
Tracking. LaSOT consists of 1,400 sequences with more than 3.5 M frames in total. Each …

Abstract The Visual Object Tracking challenge VOT2018 is the sixth annual tracker
benchmarking activity organized by the VOT initiative. Results of over eighty trackers are …

Tracking by natural language specification is a new rising research topic that aims at
locating the target object in the video sequence based on its language description …

Despite the numerous developments in object tracking, further development of current
tracking algorithms is limited by small and mostly saturated datasets. As a matter of fact, data …