Abstract
An important area of computer vision is real-time object tracking, which is now widely used in intelligent transportation and smart industry technologies. Although the correlation filter object tracking methods have a good real-time tracking effect, it still faces many challenges such as scale variation, occlusion, and boundary effects. Many scholars have continuously improved existing methods for better efficiency and tracking performance in some aspects. To provide a comprehensive understanding of the background, key technologies and algorithms of single object tracking, this article focuses on the correlation filter-based object tracking algorithms. Specifically, the background and current advancement of the object tracking methodologies, as well as the presentation of the main datasets are introduced. All kinds of methods are summarized to present tracking results in various vision problems, and a visual tracking method based on reliability is observed.
Highlights
Rapid developments of artificial intelligence and computer vision have been widely visible in various fields
We provide a comprehensive introduction to existing data sets, and summarize the current correlation filter-based object tracking algorithms to present a comparison of models sourced in the domain of correlation filter tracking
The correlation filter algorithm has the characteristics of high speed and precision, but it faces the challenges of boundary effect and scale effect
Summary
Rapid developments of artificial intelligence and computer vision have been widely visible in various fields. The model update strategy uses the traditional CF linear interpolation method: Xm(fo) del = (1 − )Xm(fo−d1e)l + Xf. Figure 9 below shows the sample training of the DCF algorithm and the BACF algorithm processing operation and effects of the improved quality. This may cause the trained filter to be dominated by unexpectedly highlighted areas on the feature map, resulting in model degradation To solve this problem, Sun et al [28] proposed a new CF-based optimization problem to jointly simulate identification and reliability information. The SRDCF and BACF algorithms have imposed additional spatial constraints on the filter coefficients, the boundary effects are mitigated to some extent These constraints are usually fixed for different objects and cannot fully utilize the diversity information of the target.
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