Abstract
In this paper, we propose to combine detections from background subtraction and from a multiclass object detector for multiple object tracking (MOT) in urban traffic scenes. These objects are associated across frames using spatial, colour and class label information, and trajectory prediction is evaluated to yield the final MOT outputs. The proposed method was tested on the Urban tracker dataset and shows competitive performances compared to state-of-the-art approaches. Results show that the integration of different detection inputs remains a challenging task that greatly affects the MOT performance.
Abstract (translated)
本文提出将背景减法和多类目标检测相结合，用于城市交通场景中的多目标跟踪。这些对象通过使用空间、颜色和类标签信息跨帧关联，并评估轨迹预测以生成最终的MOT输出。在城市跟踪器数据集上对该方法进行了测试，并与最先进的方法进行了比较，结果表明该方法具有竞争力。结果表明，不同检测输入的集成仍然是一项具有挑战性的任务，对MOT性能有很大影响。
URL
https://arxiv.org/abs/1905.06381
