CRCV researchers top the 3D Tracking Leaderboard at https://jrdb.stanford.edu/leaderboards/tracking.

You can read the extended abstract here.

“JRDB is a novel dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of multimodal sensor data including stereo cylindrical 360° RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGBD video at 30 fps, 360° spherical image from a fisheye camera and encoder values from the robot’s wheels. Our dataset includes data from traditionally underrepresented scenes such as indoor environments and pedestrian areas, from both a stationary and navigating robot platform.

The dataset has been annotated with over 2.4 million bounding boxes spread over 5 individual cameras and 1.8 million associated 3D cuboids around all people in the scenes, totalling over 3500 time consistent trajectories. The dataset has also contain the annotations representing social group membership IDs, action labels and social activity labels for all the individuals visible in every frame of RGB 360° cylindrical video stream.

Together with the JRDB dataset and annotations, we have launched seven benchmarks including 2D and 3D person detection and tracking, human social group detection, individual action detection and social activity recognition.

The goal of JRDB is to provide a new source of data and a test-bench for research in the areas of autonomous robot navigation and all perceptual tasks related to social robotics in human environments.”