Human Activity Detection from RGBD Images
Being able to detect and recognize human activities is essential for several applications, including smart homes and personal assistive robotics. In this paper, we perform detection and recognition of unstructured human activity in unstructured environments. We use a RGBD sensor (Microsoft Kinect) as the input sensor, and compute a set of features based on human pose and motion, as well as based on image and point-cloud information.
See related project on anticipating human activities.
Popular Press
E&T Magazine, Phys.org, R&D Magazine, Gizmag, GizmoWatch, myScience, WonderHowTo, Geekosystem.
Data/Code
Download Cornell Activity Datasets and Code
Publications
Learning Human Activities and Object Affordances from RGB-D Videos, Hema S Koppula, Rudhir Gupta, Ashutosh Saxena. International Journal of Robotics Research (IJRR), in press, Jan 2013. [PDF] [CAD-120 Dataset]
Unstructured Human Activity Detection from RGBD Images, Jaeyong Sung, Colin Ponce, Bart Selman, Ashutosh Saxena. International Conference on Robotics and Automation (ICRA), 2012. [PDF] [CAD-60 Dataset]
Human Activity Detection from RGBD Images, Jaeyong Sung, Colin Ponce, Bart Selman, Ashutosh Saxena. In AAAI workshop on Pattern, Activity and Intent Recognition (PAIR), 2011. [PDF] [CAD-60 Dataset]
People
| Jaeyong Sung | jysung at cs.cornell.edu |
| Hema Koppula | hema at cs.cornell.edu |
| Prof. Bart Selman | selman at cs.cornell.edu |
| Prof. Ashutosh Saxena | asaxena at cs.cornell.edu |