Name
Title | ||
|---|---|---|
Human fall detection in videos via boosting and fusing statistical features of appearance, shape and motion dynamics on Riemannian manifolds with applications to assisted living. |
Abstract | ||
|---|---|---|
Dynamic features of a falling person are represented as points moving on manifolds.Human falls are characterized by velocity statistics based on geodesics.Statistical features are fused by sub-ensemble learning under a boosting framework.Comparable results are obtained to multi-camera and multi-modal methods. Display Omitted This paper addresses issues in fall detection from videos. It is commonly observed that a falling person undergoes large appearance change, shape deformation and physical displacement, thus the focus here is on the analysis of these dynamic features that vary drastically in camera views while a person falls onto the ground. A novel approach is proposed that performs such analysis on Riemannian manifolds, detecting falls from a single camera with arbitrary view angles. The main novelties of this paper include: (a) representing the dynamic appearance, shape and motion of a target person each being points moving on a different Riemannian manifold; (b) characterizing the dynamics of different features by computing velocity statistics of their corresponding manifold points, based on geodesic distances; (c) employing a feature weighting approach, where each statistical feature is weighted according to the mutual information; (d) fusing statistical features learned from different manifolds with a two-stage ensemble learning strategy under a boosting framework. Experiments have been conducted on two video datasets for fall detection. Tests, evaluations and comparisons with 6 state-of-the-art methods have provided support to the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.cviu.2015.12.002 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
Human fall detection,Riemannian manifolds,Boosting,Elderly care,Assisted living | Computer vision,Weighting,Riemannian manifold,Artificial intelligence,Boosting (machine learning),Mutual information,Motion dynamics,Ensemble learning,Manifold,Mathematics,Machine learning,Geodesic | Journal |
Volume | Issue | ISSN |
148 | C | 1077-3142 |
Citations | PageRank | References |
4 | 0.40 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yixiao Yun | 1 | 36 | 5.09 |
| Irene Yu-Hua Gu | 2 | 613 | 35.06 |