About this challenge
Large Scale 3D Human Activity Analysis has been attracting an increasing attention. As a significant part of video understanding in the field of computer vision and multimedia, activity analysis such as Action Recognition and Action Detection remains a challenging problem. The technology will potentially facilitate a wide range of practical applications.
For human activity analysis, many great algorithms have been designed for RGB videos recorded by 2D cameras in the past couple of decades. Recently, with the prevalence of the affordable color-depth sensing cameras like Microsoft Kinect, it is much easier and cheaper to obtain depth data and the 3D skeleton of human body. As an intrinsic high level representation, 3D skeleton is valuable and comprehensive for summarizing a series of human dynamics in the video, and thus benefits the more general action analysis. Besides of succinctness and effectiveness, it shows great robustness to illumination, clustered background, and camera motion. Additionally, it is captured based on the infrared ray which can avoid the accuracy loss caused by object occlusion.
Recently, large scale data and deep learning have been revolutionizing computer vision research. To address the lack of a large scale 3D dataset for activity analysis, we build a new dataset and establish a Half-Day workshop to stimulate the computer vision community to design models and algorithms which can improve the performance of human activity analysis on 3D skeleton data
