Transformer-based fall detection in videos

Falls pose a major threat for the elderly as they result in severe consequences for their physical and mental health or even death in the worst-case scenario. Nonetheless, the impact of falls can be alleviated with appropriate technological solutions. Fall detection is the task of recognising a fall, i.e. detecting when a person has fallen in a video. Such an algorithm can be implemented in lightweight devices which can then cater to the users’ needs, e.g. alerting emergency services or caregivers. At the core of those systems, a model capable of promptly recognising falls is crucial for reducing the time until help comes. In this paper we propose a fall detection solution based on transformers, i.e. state-of-the-art neural networks for computer vision tasks. Our model takes a video clip and decides if a fall has occurred or not. In a video stream, it would be applied in a sliding-window fashion to trigger an alarm as soon as it detects a fall. We evaluate our fall detection backbone model on the large UP-Fall dataset, as well as on the UR fall dataset, and compare our results with existing literature using the former dataset.