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 this https URL .

介绍了 JRDB-Pose 数据集，该数据集是一个使用社交导航机器人拍摄的大规模数据集，并包括拥挤室内外位置和各种尺度和遮挡类型的挑战场景，可用于多人姿态估计和跟踪的基准测试，为自主机器人系统在人类环境中的操作和决策提供深入理解的支持。