Identifying unintentional falls in action videos using the 3D Cylindrical Trace Transform

Identification of unintentional falls is a critical application in smart assistive environments, especially in the context of elderly care. However, visually discriminating between falls and fall-like, intentional activities is a challenging task. In this paper, we propose the utilization of a novel feature extraction scheme based on the newly formulated 3D Cylindrical Trace Transform, on spatio-temporal interest points, for the task of fall detection. Using this pipeline, we are able to produce features invariant to occlusion, viewpoint, camera placement and other distortions. Experimentation on two publicly available datasets, on a number of different conditions, proved the efficiency of the proposed methodology for the task at hand.