Deep learning has been overwhelmingly successful in computer vision (CV), natural
language processing, and video/speech recognition. In this paper, our focus is on CV. We …

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

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 …

Recently, combining multiple features into discriminative correlation filters to improve
tracking representation has shown great potential in object tracking. Existing trackers apply …

Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …

The training of a feature extraction network typically requires abundant manually annotated
training samples, making this a time-consuming and costly process. Accordingly, we …

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 …

Existing deep trackers mainly use convolutional neural networks pre-trained for the generic
object recognition task for representations. Despite demonstrated successes for numerous …

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

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 …