论文标题
JRDB置态：用于多人姿势估计和跟踪的大型数据集
JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Tracking
论文作者
论文摘要
在人类环境中运行的自主机器人系统必须了解其周围环境，以做出准确，安全的决定。在拥挤的人类机器人相互作用和机器人导航的人类场景中，深刻的理解需要随着时间的流逝，随着时间的流逝，人体姿势估计和跟踪。但是，现有数据集要么不提供姿势注释，要么包括与机器人应用无关的场景类型。许多数据集还缺乏在拥挤的人类场景中发现的姿势和遮挡的多样性。为了解决此限制，我们介绍了JRDB置台，这是一个大规模数据集和基准测试，用于使用社交导航机器人捕获的视频进行多人姿势估算和跟踪。该数据集包含带有拥挤的室内和室外位置的挑战场景，以及各种各样的鳞片和遮挡类型。 JRDB姿势提供了人类姿势注释，并具有每次遮挡标签和整个场景一致的跟踪ID。公共评估服务器可在持有的测试集上进行公平评估。 jrdb置换可在https://jrdb.erc.monash.edu/上获得。
Autonomous robotic systems operating in human environments must understand their surroundings to make accurate and safe decisions. In crowded human scenes with close-up human-robot interaction and robot navigation, a deep understanding requires reasoning about human motion and body dynamics over time with human body pose estimation and tracking. However, existing datasets either do not provide pose annotations or include scene types unrelated to robotic applications. Many datasets also lack the diversity of poses and occlusions found in crowded human scenes. To address this limitation we introduce JRDB-Pose, a large-scale dataset and benchmark for multi-person pose estimation and tracking using videos captured from a social navigation robot. The dataset contains challenge scenes with crowded indoor and outdoor locations and a diverse range of scales and occlusion types. JRDB-Pose provides human pose annotations with per-keypoint occlusion labels and track IDs consistent across the scene. A public evaluation server is made available for fair evaluation on a held-out test set. JRDB-Pose is available at https://jrdb.erc.monash.edu/ .
