Towards Real-Time Visual Tracking with Graded Color-names Features

TL;DR

This paper introduces a real-time visual tracking method that enhances MeanShift by integrating background models and graded color-name features, improving robustness against target speed, scale changes, and occlusions.

Contribution

It develops a novel tracking approach combining background models with graded color-name features within the MeanShift framework, addressing key limitations of traditional methods.

Findings

Improved tracking accuracy in challenging scenarios

Enhanced robustness against occlusion and scale variation

Maintained real-time detection speed

Abstract

MeanShift algorithm has been widely used in tracking tasks because of its simplicity and efficiency. However, the traditional MeanShift algorithm needs to label the initial region of the target, which reduces the applicability of the algorithm. Furthermore, it is only applicable to the scene with a large overlap rate between the target area and the candidate area. Therefore, when the target speed is fast, the target scale change, shape deformation or the target occlusion occurs, the tracking performance will be deteriorated. In this paper, we address the challenges above-mentioned by developing a tracking method that combines the background models and the graded features of color-names under the MeanShift framework. This method significantly improve performance in the above scenarios. In addition, it facilitates the balance between detection accuracy and detection speed. Experimental results demonstrate the validation of the proposed method.