Abstract
Most existing perception systems rely on sensory data acquired from cameras, which perform poorly in low light and adverse weather conditions. To resolve this limitation, we have witnessed advanced LiDAR sensors become popular in perception tasks in autonomous driving applications. Nevertheless, their usage in traffic monitoring systems is less ubiquitous. We identify two significant obstacles in cost-effectively and efficiently developing such a LiDAR-based traffic monitoring system: (i) public LiDAR datasets are insufficient for supporting perception tasks in infrastructure systems, and (ii) 3D annotations on LiDAR point clouds are time-consuming and expensive. To fill this gap, we present an efficient semi-automated annotation tool that automatically annotates LiDAR sequences with tracking algorithms while offering a fully annotated infrastructure LiDAR dataset -- FLORIDA (Florida LiDAR-based Object Recognition and Intelligent Data Annotation) -- which will be made publicly available. Our advanced annotation tool seamlessly integrates multi-object tracking (MOT), single-object tracking (SOT), and suitable trajectory post-processing techniques. Specifically, we introduce a human-in-the-loop schema in which annotators recursively fix and refine annotations imperfectly predicted by our tool and incrementally add them to the training dataset to obtain better SOT and MOT models. By repeating the process, we significantly increase the overall annotation speed by three to four times and obtain better qualitative annotations than a state-of-the-art annotation tool. The human annotation experiments verify the effectiveness of our annotation tool. In addition, we provide detailed statistics and object detection evaluation results for our dataset in serving as a benchmark for perception tasks at traffic intersections.
Abstract (translated)
现有的感知系统大多数依赖于从相机获取的感官数据,这些数据在低光和不利天气条件下表现不佳。为了解决这一限制,我们见证了先进的LiDAR传感器在自主驾驶应用中的感知任务中越来越受欢迎。然而,在交通监测系统中,它们的使用相对较少。我们确定了开发这种基于LiDAR的交通监测系统 cost- effectively and efficiently 的两个重要障碍:(一)公共LiDAR数据集不足以支持基础设施系统中的感知任务,(二)在LiDAR点云上的三维标注是非常耗时且昂贵的。为了填补这一空缺,我们提出了一种高效的半自动标注工具,该工具会自动用跟踪算法对LiDAR序列进行标注,同时提供一个完整的标注基础设施LiDAR数据集——Florida(Florida基于LiDAR的对象识别和智能数据标注),并将这些信息公之于众。我们的高级标注工具无缝集成了多物体跟踪(MOT)、单物体跟踪(SOT)和适当的轨迹后处理技术。具体来说,我们引入了一个参与循环的人类标注 schema,该 schema 让标注者递归地修正和优化我们工具预测的不完美标注,并逐步将它们添加到训练数据集中,以获得更好的SOT和MOT模型。通过重复这个过程,我们显著增加了整个标注速度,比最先进的标注工具快了三到四倍,并获得了更好的标注质量。人类标注实验证实了我们的标注工具的有效性。此外,我们提供了我们数据集的详细统计信息和物体检测评估结果,以作为交通十字路口感知任务基准。
URL
https://arxiv.org/abs/2301.10732
