EnhanceCenter for improving point based tracking and rich feature representation
Abstract In this study, we propose EnhanceCenter, a multiple-object tracking model that demonstrates enhanced tracking efficiency and stability while reducing dependencies on computationally intensive detectors. EnhanceCenter, based on the CenterTrack method, introduces three key improvements. First...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88924-2 |
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| _version_ | 1850029851158773760 |
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| author | Hyun-Sung Yang Sung-Wook Park Se-Hoon Jung Chun-Bo Sim |
| author_facet | Hyun-Sung Yang Sung-Wook Park Se-Hoon Jung Chun-Bo Sim |
| author_sort | Hyun-Sung Yang |
| collection | DOAJ |
| description | Abstract In this study, we propose EnhanceCenter, a multiple-object tracking model that demonstrates enhanced tracking efficiency and stability while reducing dependencies on computationally intensive detectors. EnhanceCenter, based on the CenterTrack method, introduces three key improvements. First, a channel–spatial–spatial feature fusion module effectively utilizes object appearance information, enhancing tracking in complex scenes. Second, the backbone network weights are optimized for multiple-object tracking tasks, enabling more effective feature extraction. Lastly, an improved association method increases long-term tracking stability, maintaining consistency during occlusions or detection failures. Experiments on various MOT benchmarks demonstrated the performance of EnhanceCenter against models using high-performance detectors. On the MOT17 test set, EnhanceCenter outperformed CenterTrack with a 1.6% improvement in IDF1 and achieved a HOTA of 55.1%, surpassing leading center-point-based tracking studies, such as TransTrack and TransCenter. The MOT20 dataset showed a significant 13% improvement in IDF1 compared to CenterTrack. This research underscores the potential of lightweight detectors in achieving state-of-the-art multiple-object tracking performance, paving the way for more efficient tracking solutions in complex environments. |
| format | Article |
| id | doaj-art-23a9ff4a4b514d6fbe977e23f2f4ef24 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-23a9ff4a4b514d6fbe977e23f2f4ef242025-08-20T02:59:24ZengNature PortfolioScientific Reports2045-23222025-03-0115111410.1038/s41598-025-88924-2EnhanceCenter for improving point based tracking and rich feature representationHyun-Sung Yang0Sung-Wook Park1Se-Hoon Jung2Chun-Bo Sim3Interdisciplinary Program in IT-Bio Convergence System, Sunchon National UniversityInterdisciplinary Program in IT-Bio Convergence System, Sunchon National UniversityDepartment of Computer Engineering, Sunchon National UniversityInterdisciplinary Program in IT-Bio Convergence System, Sunchon National UniversityAbstract In this study, we propose EnhanceCenter, a multiple-object tracking model that demonstrates enhanced tracking efficiency and stability while reducing dependencies on computationally intensive detectors. EnhanceCenter, based on the CenterTrack method, introduces three key improvements. First, a channel–spatial–spatial feature fusion module effectively utilizes object appearance information, enhancing tracking in complex scenes. Second, the backbone network weights are optimized for multiple-object tracking tasks, enabling more effective feature extraction. Lastly, an improved association method increases long-term tracking stability, maintaining consistency during occlusions or detection failures. Experiments on various MOT benchmarks demonstrated the performance of EnhanceCenter against models using high-performance detectors. On the MOT17 test set, EnhanceCenter outperformed CenterTrack with a 1.6% improvement in IDF1 and achieved a HOTA of 55.1%, surpassing leading center-point-based tracking studies, such as TransTrack and TransCenter. The MOT20 dataset showed a significant 13% improvement in IDF1 compared to CenterTrack. This research underscores the potential of lightweight detectors in achieving state-of-the-art multiple-object tracking performance, paving the way for more efficient tracking solutions in complex environments.https://doi.org/10.1038/s41598-025-88924-2Multi-object trackingLightweight detectorEnhanceCenterCenterTrack |
| spellingShingle | Hyun-Sung Yang Sung-Wook Park Se-Hoon Jung Chun-Bo Sim EnhanceCenter for improving point based tracking and rich feature representation Scientific Reports Multi-object tracking Lightweight detector EnhanceCenter CenterTrack |
| title | EnhanceCenter for improving point based tracking and rich feature representation |
| title_full | EnhanceCenter for improving point based tracking and rich feature representation |
| title_fullStr | EnhanceCenter for improving point based tracking and rich feature representation |
| title_full_unstemmed | EnhanceCenter for improving point based tracking and rich feature representation |
| title_short | EnhanceCenter for improving point based tracking and rich feature representation |
| title_sort | enhancecenter for improving point based tracking and rich feature representation |
| topic | Multi-object tracking Lightweight detector EnhanceCenter CenterTrack |
| url | https://doi.org/10.1038/s41598-025-88924-2 |
| work_keys_str_mv | AT hyunsungyang enhancecenterforimprovingpointbasedtrackingandrichfeaturerepresentation AT sungwookpark enhancecenterforimprovingpointbasedtrackingandrichfeaturerepresentation AT sehoonjung enhancecenterforimprovingpointbasedtrackingandrichfeaturerepresentation AT chunbosim enhancecenterforimprovingpointbasedtrackingandrichfeaturerepresentation |