Graph Neural Networks for Cross-Camera Data Association

Code

Official project site of Graph Neural Networks for Cross-Camera Data Association (under review in IEEE TCSVT journal).

Abstract

Cross-camera image data association is essential for many multi-camera computer vision tasks, such as multi-camera pedestrian detection, multi-camera multi-target tracking, 3D pose estimation, etc.

This association task is typically stated as a bipartite graph matching problem and often solved by applying minimum-cost flow techniques, which may be computationally inefficient with large data. Furthermore, cameras are usually treated by pairs, obtaining local solutions, rather than finding a global solution at once. Other key issue is that of the affinity measurement: the widespread usage of non-learnable pre-defined distances, such as the Euclidean and Cosine ones.

This paper proposes an efficient approach for cross-cameras data-association focused on a global solution, instead of processing cameras by pairs. To avoid the usage of fixed distances, we leverage the connectivity of Graph Neural Networks, previously unused in this scope, using a Message Passing Network to jointly learn features and similarity. We validate the proposal for pedestrian multi-view association, showing results over the EPFL multi-camera pedestrian dataset. Our approach considerably outperforms the literature data association techniques, without requiring to be trained in the same scenario in which it is tested.

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Code

Github link for the available code to reproduce the results of the paper. (Available upon acceptance)

arVix paper

Link to the pre-print version of the paper.

Citation

If you find this work useful, please consider citing:

Luna, E., SanMiguel, J. C., Martínez, J. M., & Carballeira, P. (2022). Graph Neural Networks for Cross-Camera Data Association. arXiv preprint arXiv:2201.06311.

 
  @article{luna2022graph,
	  title={Graph Neural Networks for Cross-Camera Data Association},
	  author={Luna, Elena and SanMiguel, Juan C and Mart{\'\i}nez, Jos{\'e} M and Carballeira, Pablo},
	  journal={arXiv preprint arXiv:2201.06311},
	  year={2022}
}

Acknowledgement: This study has been partially supported by the Spanish Government through its TEC2017-88169-R MobiNetVideo project.