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

Object detection and tracking is one of the most important and challenging branches in
computer vision, and have been widely applied in various fields, such as health-care …

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 …

Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-
based trackers. The correlation operation is a simple fusion manner to consider the similarity …

In this paper, we present a new tracking architecture with an encoder-decoder transformer
as the key component. The encoder models the global spatio-temporal feature …

Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive
biases has recently drawn extensive interest. However, existing tracking approaches rely on …

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 …

Accurate and robust visual object tracking is one of the most challenging and fundamental
computer vision problems. It entails estimating the trajectory of the target in an image …

The fully-convolutional siamese network based on template matching has shown great
potentials in visual tracking. During testing, the template is fixed with the initial target feature …

In this work, we propose a novel adaptive spatially-regularized correlation filters (ASRCF)
model to simultaneously optimize the filter coefficients and the spatial regularization weight …