Vision-based Human Fall Classification Competition
Due to the increasing of the world aging phenomenon, solutions that allow assisted living enhancing the quality of life and independent living of elderly people are more relevant nowadays. Particularly, human falls are frequent in this population and it is considered a major health problem. Recently, fall classification systems have been proposed in the current research of ambient assisted living (AAL). Different technologies have been considered like wearables, ambient sensors, or mobile devices. However, pervasive issues are found in those approaches. In contrast, vision-based approaches for non-contact and less pervasive solutions have been studied and implemented. Cameras in particular are cheap and easy to adopt.
To promote research and adoption of the latter technologies, this competition aims to build a vision-based classifier for detecting different types of falls and other activities of daily living in camera recordings. For competition purposes, the classifier should have a vision-based approach using machine learning and/or deep learning techniques; and the winner will be the one that best ranks, in terms of the F1-score metric, in the final results of the leaderboard.
This competition can be interesting to the growing research community of ambient assisted living, mainly on abnormal behavioral analysis. Moreover, it is also attractive to any person interested in solving computer vision and machine learning challenging problems. On the other hand, solutions to this challenge can also be applied in other domains such as robotics, biomedical engineering, or human-computer interaction.
This competition is co-located at WCCI 2020.
Hiram Ponce, Lourdes Martínez-Villaseñor, Ernesto Moya-Albor, Jorge Brieva, and Karina Pérez-Daniel
Facultad de Ingeniería, Universidad Panamericana, Mexico.