Graph Convolutional Network for Multi-Target Multi-Camera Vehicle Tracking
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Abstract (arXiv)
This letter focuses on the task of Multi-Target Multi-Camera vehicle tracking. We propose to associate single-camera trajectories into multi-camera global trajectories by training a Graph Convolutional Network. Our approach simultaneously processes all cameras providing a global solution, and it is also robust to large cameras unsynchronizations. Furthermore, we design a new loss function to deal with class imbalance. Our proposal outperforms the related work showing better generalization and without requiring ad-hoc manual annotations or thresholds, unlike compared approaches.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 28 Nov 2022.
Builds on
22 of 40 references.
References · 22
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10 more
- 2019CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identificationcited by 119
- 2019Imbalance Problems in Object Detection: A Reviewcited by 118
- 2021DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Trackingcited by 31
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- 2021Tackling the Imbalance for GNNscited by 5
- 2019All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networkscited by 3
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.