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Improving the Second Association for Multi-Object Tracking by Directed Reliable Neighbor Graphs
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AI
DOI：10.1109/LSP.2025.3546896.png)
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摘要
En 中文
The second association enhances Multi-Object Tracking (MOT) by reducing missed detections and trajectory fragmentation but is limited by the poor distinguishability of low-confidence detections. To address this, we introduce interactive features leveraging Graph Neural Networks (GNNs) to enhance object distinction. Unlike existing GNN-based trackers that compute interactive features for all objects, we selectively calculate interactive features based on directed reliable neighbor graphs for objects in the second stage. These graphs include two kinds of nodes: reliable nodes (already associated in the first association) and unreliable nodes (remaining objects). Bidirectional edges between reliable nodes indicate matches, while directed edges from reliable to unreliable nodes represent neighbor relationships. These graphs are forwarded to graph attention networks to obtain interactive features combined with motion features for the second association. Experimental results on the MOT17 (65.3 in HOTA, 80.6 in IDF1) and MOT20 (63.8 in HOTA, 78.0 in IDF1) benchmark datasets demonstrate the effectiveness of our proposed tracker, particularly in HOTA and IDF1.
Keyword:
Convolution
Reliability
Feature extraction
Image edge detection
Computer network reliability
Training
Noise
Measurement
Fuses
Visualization
Appearance enhancement module
graph attention network
multi-object tracking
neighbor graph
