Abstract
Online multi-object tracking (MOT) is extremely important for high-level spatial reasoning and path planning for autonomous and highly-automated vehicles. In this paper, we present a modular framework for tracking multiple objects (vehicles), capable of accepting object proposals from different sensor modalities (vision and range) and a variable number of sensors, to produce continuous object tracks. This work is inspired by traditional tracking-by-detection approaches in computer vision, with some key differences - First, we track objects across multiple cameras and across different sensor modalities. This is done by fusing object proposals across sensors accurately and efficiently. Second, the objects of interest (targets) are tracked directly in the real world. This is a departure from traditional techniques where objects are simply tracked in the image plane. Doing so allows the tracks to be readily used by an autonomous agent for navigation and related tasks. To verify the effectiveness of our approach, we test it on real world highway data collected from a heavily sensorized testbed capable of capturing full-surround information. We demonstrate that our framework is well-suited to track objects through entire maneuvers around the ego-vehicle, some of which take more than a few minutes to complete. We also leverage the modularity of our approach by comparing the effects of including/excluding different sensors, changing the total number of sensors, and the quality of object proposals on the final tracking result.
Abstract (translated)
在线多目标跟踪（MOT）对于自动和高度自动化车辆的高级空间推理和路径规划非常重要。在本文中，我们提出了一个模块化框架，用于跟踪多个物体（车辆），能够接受来自不同传感器模态（视觉和范围）的对象建议和可变数量的传感器，以产生连续的物体轨迹。这项工作的灵感来自于计算机视觉中传统的逐个检测方法，它们存在一些关键差异 - 首先，我们在多个摄像机和不同传感器模式之间跟踪物体。这是通过准确有效地在传感器上融合对象提议来完成的。其次，在现实世界中直接跟踪感兴趣的对象（目标）。这与传统技术背道而驰，传统技术只是在图像平面中跟踪对象。这样做允许自主代理容易地使用轨道来进行导航和相关任务。 为了验证我们的方法的有效性，我们在从能够捕获全环绕信息的高感应测试台收集的真实世界高速公路数据上进行测试。我们证明我们的框架非常适合通过自我车辆周围的整个机动来跟踪物体，其中一些需要花费几分钟才能完成。我们还通过比较包含/排除不同传感器的效果，改变传感器总数以及最终跟踪结果的对象提议质量来利用我们方法的模块性。
URL
https://arxiv.org/abs/1802.08755
