| Title: | OS11-3 Kalman-YOLO Improving YOLO Tracking Performance through the Integration of a Kalman Filter for a Beach Cleaning Robot |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 326-329 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS11-3 |
| Author(s): | Rut Yatigul, Chi Jie Tan, Gamolped Prem, Eiji Hayashi |
| Publication Date: | February 13, 2025 |
| Keywords: | Computer Vision, Instance Segmentation, Kalman Filter, Multi Object Tracking, Data Association |
| Abstract: | Ocean waste poses a significant threat to both human and marine life as industries and individuals continue to dump garbage into the ocean. Sea creatures are poisoned by materials such as plastics and chemicals, which in turn contaminate humans who consume them. This paper introduces an innovative approach using Image Instance Segmentation with YOLOv8 to segment and track beach garbage. However, YOLOv8's object tracking struggles in dynamic environments with challenges like occlusion, shadows, and perspective changes in RGB frames. To address this, the author presents Kalman-YOLO, combining the Kalman Filter with YOLO for improved performance. Results show notable performance improvement, especially in tracking garbage for the Beach Cleaning Robot. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS11/OS11-3.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
(c)2008 Copyright The Regents of ALife Robotics Corporation Ltd. All Rights Reserved.