A Novel Approach Coloured Object Tracker with Adaptive Model and Bandwidth using Mean Shift Algorithm

TL;DR

This paper introduces a fast, adaptive mean-shift based colored object tracking algorithm that dynamically adjusts to size and shape changes, improving robustness and speed over traditional methods.

Contribution

It presents a novel three-phase tracking algorithm with an adaptive model, enhancing the traditional mean-shift approach for better robustness and flexibility.

Findings

The proposed method is feasible and robust.

It achieves acceptable speed compared to other algorithms.

Experimental results validate its effectiveness.

Abstract

The traditional color-based mean-shift tracking algorithm is popular among tracking methods due to its simple and efficient procedure, however, the lack of dynamism in its target model makes it unsuitable for tracking objects which have changes in their sizes and shapes. In this paper, we propose a fast novel threephase colored object tracker algorithm based on mean shift idea while utilizing adaptive model. The proposed method can improve the mentioned weaknesses of the original mean-shift algorithm. The experimental results show that the new method is feasible, robust and has acceptable speed in comparison with other algorithms.15 page,