Abstract
Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different road users that move across the field of view with different directions and speeds and that can have varying appearances and sizes. Occlusions and interactions among the different objects are expected and common due to the nature of urban road traffic. In this work, a tracking framework employing classification label information from a deep learning detection approach is used for associating the different objects, in addition to object position and appearances. We want to investigate the performance of a modern multiclass object detector for the MOT task in traffic scenes. Results show that the object labels improve tracking performance, but that the output of object detectors are not always reliable.
Abstract (translated)
城市交通中的多目标跟踪（MOT）旨在产生不同道路使用者的轨迹，这些道路使用者以不同的方向和速度在视野中移动并且可以具有不同的外观和尺寸。由于城市道路交通的性质，不同物体之间的遮挡和相互作用是预期的和共同的。在该工作中，除了对象位置和外观之外，使用来自深度学习检测方法的分类标签信息的跟踪框架用于关联不同对象。我们想研究现代多类物体探测器在交通场景中对MOT任务的性能。结果表明，对象标签提高了跟踪性能，但是对象检测器的输出并不总是可靠的。
URL
https://arxiv.org/abs/1809.02073
