Work

A Stable Multi-Object Tracking Method for Unstable and Irregular Maritime Environments

Year: 2024

Type: article

Abstract: In this study, an improved stable multi-object simple online and real-time tracking (StableSORT) algorithm that was specifically designed for maritime environments was proposed to address challenges such as camera instability and irregular object motion. Specifically, StableSORT integrates a buffered IoU (B-IoU) and an observation-adaptive Kalman filter (OAKF) into the StrongSORT framework to improve tracking accuracy and robustness. A dataset was collected along the southern coast of Korea using a small autonomous surface vehicle to capture real-world maritime conditions. On this dataset, StableSORT achieved a 2.7% improvement in HOTA, 4.9% in AssA, and 2.6% in IDF1 compared to StrongSORT, and it significantly outperformed ByteTrack and OC-SORT by 84% and 69% in HOTA, respectively. These results underscore StableSORT’s ability to maintain identity consistency and enhance tracking performance under challenging maritime conditions. The ablation studies further validated the contributions of the B-IoU and OAKF modules in maintaining identity consistency and tracking accuracy under challenging maritime conditions.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse12122252

Authors: Young-Suk Han, Jae-Yoon Jung

Institution: Kyung Hee University

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: gold

Estimated APC paid: $2,955