應用相鄰最近特徵空間轉換法於跌倒偵測｜國立中央大學博碩士論文系統

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研究生：	李新民 Hsin-Min Lee
論文名稱：	應用相鄰最近特徵空間轉換法於跌倒偵測 Fall Detection Using Nearest Neighbor Feature Line Embedding
指導教授：	范國清 Kuo-Chin Fan 陳映濃 Ying-Nong Chen
口試委員:
學位類別：	博士 Doctor
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	53
中文關鍵詞：	跌倒偵測
外文關鍵詞：	Motion history image, Nearest feature line, Nearest neighbor feature line
相關次數：	點閱：17 下載：0
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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.

Content
摘要.......................................................V
Abstract..................................................VI
誌謝.....................................................VII
Chapter 1：Introduction....................................1
1.1  Motivation............................................1
1.2  Organization of the Dissertation......................6
Chapter 2：Review of Related Works.........................7
2.1  A Review of Eigenspace Approach.......................7
2.1.1  Linear Discriminant Analysis(LDA)...................8
2.1.2  Local Structure Preserving Algorithm................9
2.1.3  Optimization of the Fisher Criterion...............11
2.1.4  Discriminative Common Vectors(DCV).................13
2.2  Nearest Feature Line Embedding (NFLE)................14
Chapter 3：The Proposed Fall Detection Mechanism..........21
3.1 Human body extraction.................................23
3.2 Optical flow in the coarse stage......................24
3.3 Motion history image in the fine stage................28
3.4 Nearest Neighbor Feature Line Embedding (NNFLE).......30
Chapter4：Experimental Results............................35
4.1. Performance of various fall detection algorithms.....36
4.2 The identification capability of coarse-to-fine verifier..................................................37
4.3 Performance evaluation of fall detection under overlapping situations....................................38
Chapter 5：Conclusions and Future Works...................39
References................................................40

List of Figure
Fig. 1: The thermal imager.................................2
Fig. 2: Image extraction results captured by (a) CCD camera, (b) thermal imager.................................2
Fig. 3: Projection of NFL.................................15
Fig. 4: Training algorithm for the NFLE transformation....19
Fig. 5: Flow diagram in training and testing the fall detector..................................................22
Fig. 6: Human body extraction. (a) Temperature gray level images, (b) binarization results, and (c) morphological closing operation results.................................23
Fig. 7: The histogram of vertical components of optical flow of (a) walking, (b) falling down.....................25
Fig. 8: The region for optical flow estimation: (a) non-overlapping, (b) overlapping..............................26
Fig. 9: Fall incident in overlapping situation. The first row is the silhouettes, the second row is the corresponding optical flow results, and the third row is the histograms of vertical components of optical flows. (a) The results generated by original method, (b) the results generated by using dividing method.....................................27
Fig. 10: MHI motion template: (a) walk, (b) fall..........28
Fig. 11: Fine stage feature vector extraction: (a) MHI of walk, (b) horizontal projection of walk MHI, (c) the obtained fine stage feature vector from walk MHI, (d) MHI of fall, (e) horizontal projection of fall MHI, and (f) the obtained fine stage feature vector from fall MHI..........29
Fig. 12: (a) An extrapolation error, (b) an interpolation error.....................................................32
Fig. 13: Training algorithm for the NNFLE transformation..33

List of Table
Table 1: The data sets used in the experiments............35
Table 2: The fall detection performance on the data set. (%).......................................................36
Table 3: The identification capability of coarse stage and fine stage of the proposed method.........................37
Table 4: The performance evaluation of fall detection under overlapping situations. (%)...............................38
                                

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