Robust multiperson tracking requires the correct associations of online detection responses with existing trajectories. In this paper, we propose to integrate multiple cues to resolve the ambiguities in data association for multiperson tracking. Unlike most existing algorithms which integrate multiple cues in the same manner for different scenes, we learn scene-specific parameters to integrate multiple cues for different scenes, as the discriminative power of each cue may vary in different scenes. The scene-specific integration parameters are learned offline by supervised learning method. Min-cost multicommodity flow is employed to solve the data association task. The edge cost of the multicommodity network, which is crucial for the data association, is determined by integrating the multiple cues extracted from the detection response based on the learned scene-specific integration parameters. The experimental results on public multiperson tracking data set demonstrate the effectiveness of the proposed scene-specific integration method.

中文翻译：

---

用于多人跟踪的特定场景多线索集成

强大的多人跟踪需要将在线检测响应与现有轨迹正确关联。在本文中，我们建议整合多个线索来解决多人跟踪数据关联中的歧义。与大多数现有算法以相同方式集成不同场景的多个线索不同，我们学习特定场景的参数来集成不同场景的多个线索，因为每个线索的判别能力在不同场景中可能会有所不同。通过监督学习方法离线学习特定场景的集成参数。采用最小成本多商品流来解决数据关联任务。多商品网络的边缘成本，对数据关联至关重要，是通过基于学习到的特定场景集成参数对从检测响应中提取的多个线索进行集成来确定的。在公共多人跟踪数据集上的实验结果证明了所提出的特定场景集成方法的有效性。