由於年紀越大的人身體反應也相對地越遲緩，使得跌倒一直成為年長者意外死亡的主要原因。自動化跌倒偵測的技術若能整合到健康照護系統可以幫助人們知道跌倒的發生，進而及時提供適當的救助，特別是在昏暗的環境中，更容易成為照顧的死角。在本研究中，一種主要用於昏暗環境中的跌倒偵測被提出。處於昏暗的環境中，亮度的突然改變使得傳統的CCD攝影機影像無法完美地擷取人體輪廓。因此我們採用了熱像儀來偵測人體。所提出的方法採用由粗略到繁複的策略。首先，在粗略的階段，從熱像儀的影像中擷取向下的光流特徵，以此識別出類似跌倒的動作。然後，在繁複的階段，從類似跌倒的動作中擷取運動歷史影像(MHI)的水平投影，應用相鄰最近特徵空間轉換法(NNFLE)來驗證該事件。實驗結果顯示，我們提出的方法即使在昏暗的環境中與多人重疊的狀況下都可以非常精確地區分出跌倒事件。;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 study, 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.
