Human Fall Detection in Videos by Fusing Statistical Features of Shape and Motion Dynamics on Riemannian Manifolds

This paper addresses issues in fall detection in videos. We propose a novel method to detect human falls from arbitrary view angles, through analyzing dynamic shape and motion of image regions of human bodies on Riemannian manifolds. The proposed method exploits time-dependent dynamic features on smooth manifolds based on the observation that human falls often involve drastically shape changes and abrupt motions as comparing with other activities. The main novelties of this paper include: (a) representing videos of human activities by dynamic shape points and motion points moving on two separate unit n-spheres, or, two simple Riemannian manifolds; (b) characterizing the dynamic shape and motion of each video activity by computing the velocity statistics on the two manifolds, based on geodesic distances; (c) combining the statistical features of dynamic shape and motion that are learned from their corresponding manifolds via mutual information. Experiments were conducted on three video datasets, containing 400 videos of 5 activities, 100 videos of 4 activities, and 768 videos of 3 activities, respectively, where videos were captured from cameras in different view angles. Our test results have shown high detection rate (average 99.38%) and low false alarm (average 1.84%). Comparisons with eight state-of-the-art methods have provided further support to the proposed method