Abstract
Accidental fall is the most prominent factor that causes the accidental death of elder people due to their slow body reaction. Automatic fall detection technology integrated in a health-care system can assist human monitoring the occurrence of fall, especially in dusky environments. In this paper, a novel fall detection system focusing mainly on dusky environments is proposed. In dusky environments, the silhouette images of human bodies extracted from conventional CCD cameras are usually imperfect due to the abrupt change of illumination. Thus, our work adopts a thermal imager to detect human bodies. The proposed approach adopts a coarse-to-fine strategy. Firstly, the downward optical flow features are extracted from the thermal images to identify fall-like actions in the coarse stage. The horizontal projection of motion history images (MHI) extracted from fall-like actions are then designed to verify the incident by the proposed nearest neighbor feature line embedding (NNFLE) in the fine stage. Experimental results demonstrate that the proposed method can distinguish the fall incidents with high accuracy even in dusky environments and overlapping situations.
Highlights
Accidental fall is the most prominent factor that causes the accidental death of elder people due to their slow body reaction
Motivated from the three problems of nearest feature line embedding (NFLE), we propose a modified NFLE
5 Conclusions In this paper, a novel fall detection mechanism based on a coarse-to-fine strategy in dusky environment is proposed
Summary
Accidental fall is the most prominent factor that causes the accidental death of elder people due to their slow body reaction. If the incidents occur in a dusky and unattended environment, people usually miss the prime time for rescue To remedy this problem, a fall detection system using a thermal imager (see Fig. 1) to capture the images of human bodies is proposed in this paper. The downward optical flow features are extracted from the thermal images to identify fall-like actions. Since the main difference between fall and other actions is the vertical component changes, our work projects the MHI horizontally to obtain a 50-dimensional feature vectors using equation (7):. The J(w) can be simplified to the following form:
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