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Open Access

ARTICLE

MDGAN-DIFI: Multi-Object Tracking for USVs Based on Deep Iterative Frame Interpolation and Motion Deblurring Using GAN Model

Manh-Tuan Ha1, Nhu-Nghia Bui2, Dinh-Quy Vu1,*, Thai-Viet Dang2,*
1 Department of Vehicle and Energy Conversion Engineering, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam
2 Department of Mechatronics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam
* Corresponding Author: Dinh-Quy Vu. Email: email; Thai-Viet Dang. Email: email
(This article belongs to the Special Issue: Advances in Video Object Tracking: Methods, Challenges, and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.077237

Received 04 December 2025; Accepted 22 January 2026; Published online 13 February 2026

Abstract

In the realm of unmanned surface vehicle (USV) operations, leveraging environmental factors to enhance situational awareness has garnered significant academic attention. Developing vision systems for USVs presents considerable challenges, mainly due to variable observational conditions and angular vibrations caused by hydrodynamic forces. The paper proposed a novel MDGAN-DIFI network for end-to-end multi-object tracking (MOT), specifically designed for camera systems mounted on USVs. Beyond enhancing traditional MOT models, the proposed MDGAN-DIFI includes preprocessing modules designed to enhance the efficiency of processing input signal quality. Initially, a Deep Iterative Frame Interpolation (DIFI) module is used to stabilize frames in the spatiotemporal domain. Next, an enhanced generative adversarial network (GAN) model is applied to reduce motion blur affecting objects within the field of view. Finally, a YOLO-CSSA architecture combines dual infrared (IR) and RGB data streams to maintain consistent performance across diverse environmental conditions. By synthesizing intermediate frames and restoring blurred details, the framework seeks to stabilize object motion trajectories and recover distinctive appearance features prior to tracking. This approach directly tackles the main causes of tracking failure in maritime environments, such as motion discontinuities and visual degradation. Experimental results demonstrate that the proposed approach outperforms conventional methods in multi-object tracking on USVs, achieving a maximum accuracy (MOTA) of 47.0% and an IDF1 score of 50.1% under challenging operational conditions. Consequently, the proposed multi-object tracking network provides a more robust foundation for subsequent detection and data association processes.

Keywords

Multi-object tracking; unmanned surface vehicles; deep interactive frame interpolation; motion blur; generative adversarial networks (GAN)

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