A Novel Approach Coloured Object Tracker with Adaptive Model and Bandwidth using Mean Shift Algorithm
This work addresses a specific limitation in object tracking for computer vision applications, but it is incremental as it builds upon the existing mean shift algorithm.
The paper tackles the problem of tracking objects with changing sizes and shapes by proposing a three-phase colored object tracker based on the mean shift algorithm with an adaptive model, resulting in a method that is feasible, robust, and has acceptable speed compared to other algorithms.
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,