Graph Neural Networks for Cross-Camera Data Association

TL;DR

This paper introduces a novel graph neural network approach for cross-camera data association that learns similarity measures and provides a global solution, outperforming traditional methods on pedestrian datasets.

Contribution

It proposes a global, learnable GNN-based method for cross-camera data association, avoiding pairwise processing and fixed distance metrics.

Findings

Outperforms existing data association techniques on EPFL pedestrian dataset.

Does not require training on the specific scenario it is tested in.

Provides an efficient, scalable solution for multi-camera data association.

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. Our code is available at \url{http://www-vpu.eps.uam.es/publications/gnn_cca}.