论文标题
多目标多摄像机车辆跟踪的图形卷积网络
Graph Convolutional Network for Multi-Target Multi-Camera Vehicle Tracking
论文作者
论文摘要
这封信侧重于多目标多摄像机车辆跟踪的任务。我们建议通过训练图形卷积网络将单相机轨迹与多相机全局轨迹相关联。我们的方法同时处理所有可提供全球解决方案的相机，并且对于大型相机不同步也是可靠的。此外，我们设计了一种新的损失功能来处理阶级失衡。与比较的方法不同，我们的提案的表现优于相关工作，而无需进行临时手动注释或阈值，而无需进行临时手动注释或阈值。
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.
