Object detection and data association are critical components in multi-object tracking (MOT) systems. Despite the fact that the two components are dependent on each other, prior work often designs detection and data association modules separately which are trained with different objectives. As a result, we cannot back-propagate the gradients and optimize the entire MOT system, which leads to sub-optimal performance. To address this issue, recent work simultaneously optimizes detection and data association modules under a joint MOT framework, which has shown improved performance in both modules. In this work, we propose a new instance of joint MOT approach based on Graph Neural Networks (GNNs). The key idea is that GNNs can model relations between variable-sized objects in both the spatial and temporal domains, which is essential for learning discriminative features for detection and data association. Through extensive experiments on the MOT15/16/17/20 datasets, we demonstrate the effectiveness of our GNN-based joint MOT approach and show the state-of-the-art performance for both detection and MOT tasks.

中文翻译：

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使用图神经网络进行联合目标检测和多目标跟踪

对象检测和数据关联是多对象跟踪 (MOT) 系统中的关键组件。尽管这两个组件相互依赖，但先前的工作通常单独设计检测和数据关联模块，这些模块以不同的目标进行训练。因此，我们无法反向传播梯度并优化整个 MOT 系统，从而导致次优性能。为了解决这个问题，最近的工作在联合 MOT 框架下同时优化检测和数据关联模块，这在两个模块中都显示出改进的性能。在这项工作中，我们提出了一种基于图神经网络 (GNN) 的联合 MOT 方法的新实例。关键思想是 GNN 可以在空间和时间域中对可变大小对象之间的关系进行建模，这对于学习用于检测和数据关联的判别特征至关重要。通过对 MOT15/16/17/20 数据集的大量实验，我们证明了我们基于 GNN 的联合 MOT 方法的有效性，并展示了检测和 MOT 任务的最新性能。