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<p>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.</p>

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EnhanceCenter for improving point based tracking and rich feature representation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月3日

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  • Hyun-Sung Yang,
  • Sung-Wook Park,
  • Se-Hoon Jung,
  • Chun-Bo Sim

摘要

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.