EuroCity Persons 2.0: A Large and Diverse Dataset of Persons in Traffic

Sebastian Krebs;Markus Braun;Dariu M. Gavrila
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Abstract

We present the EuroCity Persons (ECP) 2.0 dataset, a novel image dataset for person detection, tracking and prediction in traffic. The dataset was collected on-board a vehicle driving through 29 cities in 11 European countries. It contains more than 250K unique person trajectories, in more than 2.0M images and comes with a size of 11 TB. ECP2.0 is about one order of magnitude larger than previous state-of-the-art person datasets in automotive context. It offers remarkable diversity in terms of geographical coverage, time of day, weather and seasons. We discuss the novel semi-supervised approach that was used to generate the temporally dense pseudo ground-truth (i.e., 2D bounding boxes, 3D person locations) from sparse, manual annotations at keyframes. Our approach leverages auxiliary LiDAR data for 3D uplifting and vehicle inertial sensing for ego-motion compensation. It incorporates keyframe information in a three-stage approach (tracklet generation, tracklet merging into tracks, track smoothing) for obtaining accurate person trajectories. We validate our pseudo ground-truth generation approach in ablation studies, and show that it significantly outperforms existing methods. Furthermore, we demonstrate its benefits for training and testing of state-of-the-art tracking methods. Our approach provides a speed-up factor of about 34 compared to frame-wise manual annotation. The ECP2.0 dataset is made freely available for non-commercial research use.
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欧洲城市人员 2.0:一个庞大而多样的交通参与者数据集。
我们介绍了欧洲城市人员(ECP)2.0 数据集,这是一个用于交通中人员检测、跟踪和预测的新型图像数据集。该数据集是在一辆驶过 11 个欧洲国家 29 座城市的汽车上收集的。该数据集包含超过 250K 个独特的人物轨迹,图片超过 200 万张,大小为 11 TB。ECP2.0 比以往最先进的汽车人物数据集大一个数量级。该数据集在地理覆盖范围、时间、天气和季节方面具有显著的多样性。我们讨论了一种新颖的半监督方法,该方法用于从关键帧的稀疏手动注释中生成时间上密集的伪地面实况(即二维边界框、三维人物位置)。我们的方法利用辅助激光雷达数据进行三维抬升,并利用车辆惯性传感进行自我运动补偿。它将关键帧信息纳入一个三阶段方法(轨迹子生成、轨迹子合并为轨迹、轨迹平滑),以获得准确的人物轨迹。我们在消融研究中验证了我们的伪地面实况生成方法,结果表明它明显优于现有方法。此外,我们还证明了这种方法在训练和测试最先进的跟踪方法方面的优势。与逐帧人工标注相比,我们的方法提高了约 34 倍的速度。ECP2.0 数据集可免费用于非商业研究用途。
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