论文标题
稀疏消息传递网络与特征集成在线多个对象跟踪
Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking
论文作者
论文摘要
现有的多个对象跟踪（MOT）方法设计复杂的体系结构，以更好地跟踪性能。但是，如果没有适当的输入信息组织，他们仍然无法执行稳健的跟踪并遭受频繁的身份开关。在本文中，我们提出了两种新颖的方法以及一个简单的在线消息传递网络（MPN）来解决这些限制。首先，我们探索图形节点和边缘嵌入的不同集成方法，并提出了一个新的IOU（联合交集）引导函数，从而改善了长期跟踪并处理身份开关。其次，我们引入了一种层次抽样策略，以构建稀疏图，该图可以将培训集中在更困难的样本上。实验结果表明，具有这两个贡献的简单在线MPN可以比许多最新方法更好。此外，我们的关联方法可以很好地概括，还可以改善基于私人检测方法的结果。
Existing Multiple Object Tracking (MOT) methods design complex architectures for better tracking performance. However, without a proper organization of input information, they still fail to perform tracking robustly and suffer from frequent identity switches. In this paper, we propose two novel methods together with a simple online Message Passing Network (MPN) to address these limitations. First, we explore different integration methods for the graph node and edge embeddings and put forward a new IoU (Intersection over Union) guided function, which improves long term tracking and handles identity switches. Second, we introduce a hierarchical sampling strategy to construct sparser graphs which allows to focus the training on more difficult samples. Experimental results demonstrate that a simple online MPN with these two contributions can perform better than many state-of-the-art methods. In addition, our association method generalizes well and can also improve the results of private detection based methods.
