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研究生:陳紹瑜
研究生(外文):Chen,Shao-Yu
論文名稱:基於視覺的即時跌倒偵測通報系統
論文名稱(外文):Vision-Based Real-time Fall Detection Notification System
指導教授:陳伯岳陳伯岳引用關係
指導教授(外文):Chen,Bo-Yue
口試委員:陳伯岳施明毅馬尚智
口試委員(外文):Chen,Bo-YueShi,Ming-YiMa,Shang-Zhi
口試日期:2021-06-29
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:55
中文關鍵詞:影像辨識跌倒偵測背景分離二值化邊緣偵測
外文關鍵詞:Image recognitionFall detectionBackground subtractionBinarizationEdge detection
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在未來幾十年,人類社會正面臨著高齡化問題,老年人口的比例增加。根據內政部統計,我國老年人口比已達15.5%。老人的健康已成為被關注的議題。而跌倒對老人來說被認為是最危險的事故之一,本文提出了一種使用監視器的非接觸式方法偵測人體狀態,其具有易於實施、成本低、高檢測準確度優點。為了實現即時偵測,比起簡單的運動分析,系統通過簡單的長寬比辨識跌倒發生的可能性並減少分析不相關的數據。此外,本文透過「跌倒偵測」,結合「LINE Notify」將既有監視器升級成為「即時跌倒偵測系統」的智能裝置。當跌倒發生時,「即時跌倒偵測系統」可立即透過影像辨識,偵測跌倒發生,並將警告訊息與截圖畫面即時傳送到照護者的手機通訊軟體中,提醒其注意或提供援助。此外,本文系統保障老人的隱私權,因為它是完全自動化的,除了緊急情況外,沒有人可以看到影像。實驗結果表明本文的跌倒偵測系統具有高度的可行性,和很高的準確度。
In the next decades, human society is facing the problem of ageing, and the proportion of the elderly population is increasing. According to the data from Ministry of the Interior, the current proportion of the elderly population has reached 15.5%. The health of the elderly has become the focus of issue. Falling is considered as one of the most parlous accidents for the elderly. This paper proposes a non-contact method using monitors to detect human body state, which has the advantages of easy implementation, low cost and high detection accuracy. To achieve real-time detection, rather than common pose analysis, the system identifies the possibility of falling through simple aspect ratio and reduces the analysis of irrelevant data. In addition, this paper uses "Falling Detection" combined with "LINE Notify" to upgrade the existing monitor to a smart device of the "Real-time Fall Detection System".When a fall occurs, the "Real-time Fall Detection System" can immediately detect the fall through the image-recognition, and send a warning message to the caregiver's mobile phone communication software to remind them to pay attention or provide assistance. In addition, this system protects the privacy of the elderly because it is completely automated, and no one has access to the images except for emergencies. Experiments showed that the proposed fall detection system is highly feasible and performs high accuracy.
摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
第一節 研究動機與目的 2
第二節 研究方法與步驟 4
第三節 本文架構 6
第二章 文獻探討 7
第一節 基於背景分離之跌倒偵測方法 7
第二節 基於深度學習之跌倒偵測方法 11
第三節 基於物件辨識之跌倒偵測方法 14
第四節 文獻探討結論 15
第三章 系統架構 17
第一節 前景擷取 19
壹、高斯混合背景模型 19
貳、背景分離 22
參、影像增強 24
第二節 特徵提取 31
第三節 跌倒判斷 32
第四章 實驗結果與討論 35
第一節 系統概述 35
壹、 硬體設備與工作環境 35
貳、 軟體開發套件 37
第二節 實驗場景 38
第三節 影像前景擷取 40
第四節 特徵提取 42
第五節 跌倒偵測 43
第五章 結論與未來展望 49
第一節 結論 49
第二節 未來展望 50
參考文獻 51
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