Falls pose severe risks to vulnerable populations, particularly the elderly and individuals with adverse neurological conditions, necessitating reliable and non-obstructive detection systems. While previous multi-modal approaches utilizing video and audio have demonstrated strong performance, they face significant limitations regarding sensitivity to environmental noise. This paper presents a robust, video-only fall detection framework that eliminates reliance on acoustic data to enhance universality. We conduct a comprehensive comparative analysis of five optical flow (OF) algorithms—Horn–Schunck, Lucas–Kanade (LK), LK-Derivative of Gaussian, Farneback, and the spectral method SOFIA—to determine the range of applicability of each technique for capturing fall dynamics. Beyond detection accuracy, we investigate the computational efficiency of each configuration. This optimized, privacy-centric pipeline offers a scalable solution for continuous monitoring in home and clinical settings, addressing the critical need for immediate intervention following high-impact falls.
Karpuzov et al. (Fri,) studied this question.