CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations
Jawad Tayyub, Majd Hawasly, Hogg, David C., Anthony G. Cohn · arXiv (Cornell University) · 2017
An activity dataset is presented here which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions. The dataset consists of a set of videos of actors performing everyday activities in a natural and unscripted manner. The dataset was recorded using a static Kinect 2 sensor which is commonly used on many robotic platforms. The dataset comprises of RGB-D images, point cloud data, automatically generated skeleton tracks in addition to crowdsourced annotations. We believe that this dataset is particularly suitable as a testbed for activity recognition research but it can also be applicable for other common tasks in robotics/computer vision research such as object detection and human skeleton tracking.