This is the third workshop from the JRDB workshop series, tailored to many perceptual problems for an autonomous robot to operate, interact and navigate in human environments. These perception tasks include any 2D or 3D visual scene understating problem as well as any other problems pertinent to human action, intention and social behaviour understanding such as 2D-3D human detection, tracking and forecasting, 2D-3D human body skeleton pose estimation, tracking and forecasting and human social grouping and activity recognition.
JRDB dataset contains 67 minutes of the annotated sensory data acquired from the JackRabbot mobile manipulator and includes 54 indoor and outdoor sequences in a university campus environment. The sensory data includes a stereo RGB 360° cylindrical video stream, 3D point clouds from two LiDAR sensors, audio and GPS positions. In our first workshop, we introduced JRDB including annotations for 2D bounding boxes and 3D oriented cuboids around pedestrians. In our second workshop, we further introduced new annotations for individual actions, human social group formation, and social activity of each social group. In this workshop, we additionally release anontations for 2D human body pose including 800,000+ annotated human body skeletons with visibility and occlusion labels. We also have, as invited speakers, world-renowned experts in the field of visual perceptions for understanding human action, behaviour, shape, and pose.
We invite researchers to submit their papers addressing topics related to autonomous (robot) navigation in human environments. Relevant topics include, but not limited to:
Submissions could follow the ECCV format (maximum 14 single-column pages excluding references) with the submission deadline of June 29, or extended abstract (maximum 2 page, single-column excluding references) with the submission deadline of TBD. Accepted papers have the opportunity to be presented as a poster during the workshop. However, only papers in ECCV format will appear in the proceedings. By submitting to this workshop, the authors agree to the review process and understand that we will do our best to match papers to the best possible reviewers. The reviewing process is double-blind. Submission to the challenge is independent of the paper submission, but we encourage the authors to submit to one of the challenges.
|Start Time||End Time||Description|
|12:30 PM||12:40 PM||Introduction|
|12:40 PM||13:10 PM||Invited Talk|
|13:10 PM||13:30 PM||Full Paper Oral Presentations|
|13:30 PM||14:00 PM||Invited Talk|
|14:00 PM||14:30 PM||Coffee Break & Video Demo Session|
|14:30 PM||15:00 PM||Invited Talk|
|15:00 PM||15:30 PM||Invited Talk|
|15:30 PM||15:45 PM||Introduction to JRDB Pose Dataset and Challenge|
|15:45 PM||16:10 PM||Challenge Winners' Presentation|
|16:40 PM||16:50 PM||Discussion, Closing Remarks and Awards|
In addition to the existing benchmarks and challenges on JRDB (2D-3D person detection and tracking, human social group identification, individual action detection, and social activity recognition), in this workshop, we organise two new challenges using our new annotations:
The first winner of each of the challenges will be awarded a prize (TBD)
and a certificate. The winners will also have an opportunity to present their work as a
spotlight (5 minutes) and poster presentation during the workshop.
Now, in addition to all of the annotation above, we introduce a new set of annotations for human body pose including:
For more details about the dataset, see here.
The participants should strictly follow the same submission
policy provided in the main JRDB webpage, which can be found here.
Also, in order to distinguish the challenge submissions from the other regular submissions,
submission name should be followed by a ECCV22 tag, e.g., "submissionname_ECCV22".
ignore those submissions for the challenge.
Each challenge submission should be followed by an extended abstract submission via our CMT webpage (the
details are available below) or a link to an existing Arxiv preprint/publication.
We use the first metric after "name" in all the leaderboards as the main evaluation for ranking the entries. For each benchmark, we have also created toolkits to work with the dataset, perform evaluation, and create submissions. These toolkits are available at here.
Professor of Computer Science, University of Tübingen
Associate Professor in the Robotics Institute, Carnegie Mellon University and Director, Facebook Reality Lab
Professor of Computer Vision at the University of Bristol
Professor of Computer Science, University of Tübingen
Professor at Monash University
Head of Machine Learning Research at Toyota Research Institute (TRI)
|Aakash Kumar||University of Central Florida|
|Dan Jia||RWTH Aachen University|
|Edwin Pan||Stanford University|
|Ehsan Adeli||Stanford University|
|Haofei Xu||University of Tübingen|
|Huangying Zhan||The University of Adelaide|
|Karttikeya Mangalam||UC Berkeley|
|Michael Wray||University of Bristol|
|Michael Villamizar||Idiap Research Institute|
|Nathan Tsoi||Yale University|
|Nikos Athanasiou||Max Planck Institute for Intelligent Systems|
|Shyamal Buch||Stanford University|
|Tianyu Zhu||Monash University|
|Vida Adeli||University of Toronto|
|Vineet Kosaraju||Stanford University|
|Ye Yuan||Carnegie Mellon University|
The University of Adelaide