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.

实时的对象跟踪是计算机视觉的重要领域，目前已广泛应用于智能交通和智能行业技术中。尽管相关滤波器对象跟踪方法具有良好的实时跟踪效果，但是它仍然面临许多挑战，例如尺度变化，遮挡和边界效应。许多学者不断改进现有方法，以在某些方面提高效率和跟踪性能。为了全面了解单个对象跟踪的背景，关键技术和算法，本文重点介绍基于相关滤波器的对象跟踪算法。具体来说，介绍了对象跟踪方法的背景和当前进展以及主要数据集的表示方式。