论文标题
深度多拍的网络，用于建模多人跟踪应用中的外观相似性
Deep Multi-Shot Network for modelling Appearance Similarity in Multi-Person Tracking applications
论文作者
论文摘要
在实际不受限制的情况下，多对象跟踪的自动化成为一项艰巨的任务，在这种情况下，算法必须与人群，越过人，遮挡，失踪和视觉上相似的个体的存在打交道。在这种情况下，传入检测及其相应身份之间的数据关联可能会错过某些轨道或产生身份开关。为了减少这些跟踪错误，甚至在其他框架中的传播，本文提出了一个深度的多拍神经模型，用于测量人观察之间的外观相似程度（MS-DOAS）。该模型为个人的外观表示提供了时间一致性，并提供了一个亲和力指标来执行逐帧数据关联，从而允许在线跟踪。该模型经过了故意培训，以便能够管理以前的身份开关的存在和在处理轨道中遗漏的观察结果。出于这个目的，已经设计了一种新颖的数据生成工具来创建模拟此类情况的训练轨迹。该模型表明，当新观测值对应于某个轨道时，该模型具有很高的辨别能力，在硬测试中达到了97 \％的分类精度，以模拟以前的错误。此外，通过将该模型的跟踪效率集成到逐个检测算法的逐帧关联中，可以证明该模型的跟踪效率。
The automatization of Multi-Object Tracking becomes a demanding task in real unconstrained scenarios, where the algorithms have to deal with crowds, crossing people, occlusions, disappearances and the presence of visually similar individuals. In those circumstances, the data association between the incoming detections and their corresponding identities could miss some tracks or produce identity switches. In order to reduce these tracking errors, and even their propagation in further frames, this article presents a Deep Multi-Shot neural model for measuring the Degree of Appearance Similarity (MS-DoAS) between person observations. This model provides temporal consistency to the individuals' appearance representation, and provides an affinity metric to perform frame-by-frame data association, allowing online tracking. The model has been deliberately trained to be able to manage the presence of previous identity switches and missed observations in the handled tracks. With that purpose, a novel data generation tool has been designed to create training tracklets that simulate such situations. The model has demonstrated a high capacity to discern when a new observation corresponds to a certain track, achieving a classification accuracy of 97\% in a hard test that simulates tracks with previous mistakes. Moreover, the tracking efficiency of the model in a Surveillance application has been demonstrated by integrating that into the frame-by-frame association of a Tracking-by-Detection algorithm.
