迈向城市共享区域中人机交互的大规模融合与标注人体姿态数据集
Towards a large-scale fused and labeled dataset of human pose while interacting with robots in shared urban areas
AI总结：
本文通过融合标注MOT17和NCLT数据集构建了城市共享区域人机交互人体姿态数据集，利用YOLOv7进行姿态估计，并引入MSJE新指标克服距离偏差，验证了该方法在室内外场景的有效性。
摘要：
在过去十年中，Autonomous Delivery Robots (ADRs，自主配送机器人) 改变了传统的配送方式，以应对不断增长的电子商务需求。然而，ADRs 是否准备好在城市共享区域中于行人之间安全导航，仍是一个悬而未决的问题。我们认为，在理解它们在此类环境中与行人的交互方面，存在关键的研究空白。Human Pose Estimation (人体姿态估计) 是各种下游应用的重要基石，包括姿态预测和具备社会意识的机器人路径规划。然而，缺乏一个能够捕捉城市共享区域中人机交互的丰富且带有姿态标注的数据集，阻碍了这一目标的实现。在本文中，我们通过重新利用、融合和标注两个数据集 MOT17 和 NCLT 来填补这一空白，这两个数据集分别专注于行人跟踪和 Simultaneous Localization and Mapping (SLAM，同步定位与建图)。由此产生的独特数据集代表了数千个真实的室内和室外人机交互场景。利用 YOLOv7，我们获得了人体姿态的视觉和数值输出，并通过手动标注提供了真实姿态。为了克服传统 MPJPE 指标中存在的距离偏差，本研究通过将边界框尺寸纳入其中，引入了一种名为 Mean Scaled Joint Error (MSJE，平均缩放关节误差) 的新型人体姿态估计误差指标。研究结果表明，YOLOv7 能够在两个数据集中有效估计人体姿态。然而，在特定场景下，例如室内、有聚光源的拥挤场景中，其表现较弱，其中 MPJPE 和 MSJE 分别记录为 10.89 和 25.3。相比之下，YOLOv7 在单人估计 (NCLT seq 2) 和室外场景 (MOT17 seq1) 中表现更好，分别达到了 5.29 和 3.38 的 MSJE 值。
英文摘要：
Over the last decade, Autonomous Delivery Robots (ADRs) have transformed conventional delivery methods, responding to the growing e-commerce demand. However, the readiness of ADRs to navigate safely among pedestrians in shared urban areas remains an open question. We contend that there are crucial research gaps in understanding their interactions with pedestrians in such environments. Human Pose Estimation is a vital stepping stone for various downstream applications, including pose prediction and socially aware robot path-planning. Yet, the absence of an enriched and pose-labeled dataset capturing human-robot interactions in shared urban areas hinders this objective. In this paper, we bridge this gap by repurposing, fusing, and labeling two datasets, MOT17 and NCLT, focused on pedestrian tracking and Simultaneous Localization and Mapping (SLAM), respectively. The resulting unique dataset represents thousands of real-world indoor and outdoor human-robot interaction scenarios. Leveraging YOLOv7, we obtained human pose visual and numeric outputs and provided ground truth poses using manual annotation. To overcome the distance bias present in the traditional MPJPE metric, this study introduces a novel human pose estimation error metric called Mean Scaled Joint Error (MSJE) by incorporating bounding box dimensions into it. Findings demonstrate that YOLOv7 effectively estimates human pose in both datasets. However, it exhibits weaker performance in specific scenarios, like indoor, crowded scenes with a focused light source, where both MPJPE and MSJE are recorded as 10.89 and 25.3, respectively. In contrast, YOLOv7 performs better in single-person estimation (NCLT seq 2) and outdoor scenarios (MOT17 seq1), achieving MSJE values of 5.29 and 3.38, respectively.
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