Learning to detect fallen people in virtual worlds

Falling is one of the most common causes of injury in all ages, especially in the elderly, where it is more frequent and severe. For this reason, a tool that can detect a fall in real time can be helpful in ensuring appropriate intervention and avoiding more serious damage. Some approaches available in the literature use sensors, wearable devices, or cameras with special features such as thermal or depth sensors. In this paper, we propose a Computer Vision deep-learning based approach for human fall detection based on largely available standard RGB cameras. A typical limitation of this kind of approaches is the lack of generalization to unseen environments. This is due to the error generated during human detection and, more generally, due to the unavailability of large-scale datasets that specialize in fall detection problems with different environments and fall types. In this work, we mitigate these limitations with a general-purpose object detector trained using a virtual world dataset in addition to real-world images. Through extensive experimental evaluation, we verified that by training our models on synthetic images as well, we were able to improve their ability to generalize. Code to reproduce results is available at https://github.com/lorepas/fallen-people-detection.