Modern trackers based on traditional image processing or machine learning have made
great progress in recent years but still face the problem of error tracking. To make use of …

Aiming at the problems of kernelized correlation filter tracking algorithm such as scale
change, severe occlusion and tracking failure under similar target tracking, a long-term …

Recently, kernelized correlation filter (KCF) has been a popular tracker for high accuracy
and robustness with high speed. However, KCF tracks objects with a fixed size template …

During the tracking, kernelized correlation filters cannot handle this situation well when the
target occluded seriously, changes scale and goes out of view. To solve the above problem …

Correlation filters tracking algorithm, one of the famous tracking algorithm, has been widely
explored in visual tracking domain because of its high accuracy. In this work, we proposed a …

Abstract Kernelized Correlation Filter (KCF) is one of state-of-the-art trackers. However, KCF
suffers from the drifting problem due to inaccurate localization caused by the scale variation …

The traditional kernel correlation filter (KCF) algorithm has poor tracking results in complex
scenes with severe occlusion, deformation, and low resolution and cannot achieve long …

In this paper, a tracking algorithm based on collaborative learning Kernelized Correlation
Filter is proposed. According to the framework of KCF, two trackers based on HoG and CN …

Correlation filter-based trackers exploit large numbers of cyclically shifted samples to train
the object classifier, which can achieve good results in tracking accuracy and speed …

Recently, many tracking algorithms based on correlation filtering are proposed, and good
performance is obtained.. Although these algorithms have extremely high accuracy, they …