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<p>Multi-Object Tracking (MOT), an essential task in computer vision, underperforms when existing occlusions or motion blurs, which will cause changes in the object’s appearances. We develop three modules based on Graph Neural Networks (GNNs) to handle such appearance changes. The appearance enhancement module boosts appearance features by applying self-attention and Graph Convolutional Neural Network (GCNN) to the local features. The temporal feature updating module automatically updates a tracklet appearance template using GCNNs with different Laplacian operations. The spatial feature updating module encodes interactive spatial features by combining a graph attention network and a GCNN. After processing input video frames with these three modules, our tracker stores all extracted features in a memory bank and then forwards them to a matching algorithm to complete tracking. Using popular benchmark datasets MOT16, MOT17, and MOT20, we show that introducing GNNs to MOT benefits tracking, and the proposed tracker surpasses the state-of-the-art trackers, including StrongSORT, ByteTrack, and BoT-SORT. Specifically, we can achieve 81.1% (77.9%) in MOTA, 80.3% (77.3%) in IDF1, and 65.1% (63.2%) in HOTA on the challenging MOT17 (or the newest MOT20) datasets.</p>

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Multi-object tracking based on graph neural networks

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Yubo Zhang,
  • Liying Zheng,
  • Qingming Huang

摘要

Multi-Object Tracking (MOT), an essential task in computer vision, underperforms when existing occlusions or motion blurs, which will cause changes in the object’s appearances. We develop three modules based on Graph Neural Networks (GNNs) to handle such appearance changes. The appearance enhancement module boosts appearance features by applying self-attention and Graph Convolutional Neural Network (GCNN) to the local features. The temporal feature updating module automatically updates a tracklet appearance template using GCNNs with different Laplacian operations. The spatial feature updating module encodes interactive spatial features by combining a graph attention network and a GCNN. After processing input video frames with these three modules, our tracker stores all extracted features in a memory bank and then forwards them to a matching algorithm to complete tracking. Using popular benchmark datasets MOT16, MOT17, and MOT20, we show that introducing GNNs to MOT benefits tracking, and the proposed tracker surpasses the state-of-the-art trackers, including StrongSORT, ByteTrack, and BoT-SORT. Specifically, we can achieve 81.1% (77.9%) in MOTA, 80.3% (77.3%) in IDF1, and 65.1% (63.2%) in HOTA on the challenging MOT17 (or the newest MOT20) datasets.