DOI : https://doi.org/10.18517/ijaseit.14.5.20444

Advanced Object Tracking through Conditional Online Updates and Noise Suppression

Suchang Lim (1), Jongchan Kim (2)
(1) Department of Computer Engineering, Sunchon National University, 235, Jungang-ro, Suncheon-si, Jeollanam-do, Republic of Korea
(2) Department of Computer Engineering, Sunchon National University, 235, Jungang-ro, Suncheon-si, Jeollanam-do, Republic of Korea
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How to cite (IJASEIT) :
Lim, Suchang, and Jongchan Kim. “Advanced Object Tracking through Conditional Online Updates and Noise Suppression”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 5, Oct. 2024, pp. 1596-01, doi:10.18517/ijaseit.14.5.20444.
Citation Format :
Object tracking under complex environmental conditions, such as background clutter, occlusion, and rapid motion, presents significant challenges. This paper addresses these issues by proposing a tracking algorithm integrating background suppression, target region enhancement, and an adaptive online template update mechanism to improve tracking accuracy. The proposed method uses the initial bounding box of the target object as a reference template and selectively updates specific regions online to suppress noise and retain critical features. We evaluated the proposed method using the OTB dataset to validate it. The baseline model without the proposed method showed a success rate of 0.417 and a precision of 0.586, while the algorithm with the proposed method achieved improved values of 0.524 and 0.728, respectively. Qualitative evaluations further confirmed the robustness of the proposed method, demonstrating high performance in scenarios with occlusion and complex backgrounds. Rather than updating all regions indiscriminately, the proposed method selectively updates the template using representative values from the target object's information. This selective update mechanism ensures the incorporation of the most relevant and accurate features, enabling the algorithm to adapt to changes in the target's appearance while minimizing noise integration. Emphasizing the feature regions and suppressing noise are also critical for maintaining a clear and precise representation of the target object, reducing the likelihood of confusion by irrelevant background information. Future research will focus on developing balanced update strategies that integrate new information while maintaining stable and reliable target characteristics.

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