In this article, a robust long-term object tracking algorithm is proposed. It can tackle the
challenges of scale and rotation changes during the long-term object tracking for security …

Kernelized correlation filter (KCF) is a kind of efficient method for real-time tracking, but
remains being challenged by the drifting problem due to inaccurate localization caused by …

The efficient and accurate tracking of a target in complex scenes has always been one of the
challenges to tackle. At present, the most effective tracking algorithms are basically neural …

Object tracking is an important capability for robots tasked with interacting with humans and
the environment, and it enables robots to manipulate objects. In object tracking, selecting …

Object tracking is a difficult work in complex situations including crowded environment,
occlusion, out of view, and fast motion. Recently, many tracking strategies have been …

Computer vision has received a significant attention in recent years, which is one of the
important parts for robots to apperceive external environment. Discriminative Correlation …

Due to the impressive performance and computational efficiency of correlation filter (CF)
based object tracking methods, CF trackers have gained lots of popularity in recent years …

In this work, we proposed a long-term tracking strategy to deal with the occlusion, out-of-
plane rotation, and the confusing non-target object. Our tracking system is composed of two …

Despite the considerable advances that are emerged in correlation filter-based tracking, in
fact, they may achieve excellent performance in robustness, speed, and accuracy; they still …

In this paper, we propose a novel tracking method by formulating tracking as a correlation
filtering as well as a ridge regression problem. First, we develop a tight correlation filter …