Abstract
This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios. Using images from a monocular camera alone, we devise pairwise costs for object tracks, based on several 3D cues such as object pose, shape, and motion. The proposed costs are agnostic to the data association method and can be incorporated into any optimization framework to output the pairwise data associations. These costs are easy to implement, can be computed in real-time, and complement each other to account for possible errors in a tracking-by-detection framework. We perform an extensive analysis of the designed costs and empirically demonstrate consistent improvement over the state-of-the-art under varying conditions that employ a range of object detectors, exhibit a variety in camera and object motions, and, more importantly, are not reliant on the choice of the association framework. We also show that, by using the simplest of associations frameworks (two-frame Hungarian assignment), we surpass the state-of-the-art in multi-object-tracking on road scenes. More qualitative and quantitative results can be found at the following URL: https://junaidcs032.github.io/Geometry_ObjectShape_MOT/.
Abstract (translated)
本文介绍了城市驾驶场景下多目标跟踪的几何形状和目标形状以及姿态成本。单独使用来自单目相机的图像，我们基于几个3D线索（例如物体姿势，形状和运动）设计物体轨迹的成对成本。所提出的成本对于数据关联方法是不可知的，并且可以结合到任何优化框架中以输出成对数据关联。这些成本易于实施，可以实时计算，并相互补充，以解决逐个检测框架中可能出现的错误。我们对设计成本进行了广泛的分析，并在不同条件下凭经验展示了对现有技术的持续改进，这些条件采用了一系列物体探测器，展示了各种相机和物体运动，更重要的是，不是依赖于关联框架的选择。我们还表明，通过使用最简单的关联框架（两帧匈牙利任务），我们超越了道路场景中多物体跟踪的最新技术。可以在以下URL找到更多定性和定量结果：https：//junaidcs032.github.io/Geometry_ObjectShape_MOT/。
URL
https://arxiv.org/abs/1802.09298
