Vehicle tracking is an essential task in the multi-object tracking (MOT) field. A distinct characteristic in vehicle tracking is that the trajectories of vehicles are fairly smooth in both the world coordinate and the image coordinate. Hence, models that capture motion consistencies are of high necessity. However, tracking with the standalone motion-based trackers is quite challenging because targets could get lost easily due to limited information, detection error and occlusion. Leveraging appearance information to assist object re-identification could resolve this challenge to some extent. However, doing so requires extra computation while appearance information is sensitive to occlusion as well. In this paper, we try to explore the significance of motion patterns for vehicle tracking without appearance information. We propose a novel approach that tackles the association issue for long-term tracking with the exclusive fully-exploited motion information. We address the tracklet embedding issue with the proposed reconstruct-to-embed strategy based on deep graph convolutional neural networks (GCN). Comprehensive experiments on the KITTI-car tracking dataset and UA-Detrac dataset show that the proposed method, though without appearance information, could achieve competitive performance with the state-of-the-art (SOTA) trackers. The source code will be available at https://github.com/GaoangW/LGMTracker.
翻译:车辆跟踪是多目标跟踪(MOT)领域的一项基本任务。车辆跟踪的一个明显特点是,车辆跟踪的轨迹在世界坐标和图像坐标上都相当平稳。因此,非常有必要使用模型来捕捉运动的构成。然而,与独立的机动跟踪器进行跟踪非常困难,因为由于信息、检测错误和隔离有限,目标很容易丢失。利用外观信息协助物体重新识别,可以在一定程度上解决这一挑战。然而,在车辆跟踪的外观信息对隐蔽性敏感时,需要额外计算。在本文中,我们试图探索车辆跟踪运动模式的重要性,而不提供外观信息。我们提出了一种新颖的方法,解决长期跟踪关联问题,利用专用的完全开发运动信息。我们用深图图图图图的电导网络(GCN)解决了嵌入式战略问题。关于KITTI-car跟踪数据集和UA-Detrac数据集的全面实验表明,拟议的方法尽管没有外观,但可在无外观的情况下实现ATA/TA轨道的竞争性运行。