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研究生： 劉力葶
Li-Ting Liu
論文名稱： 基於視覺及系統單晶片之新型且穩健的跌倒偵測感測器
Novel and Robust Vision- and SoC-Based Sensor for Fall Detection
指導教授： 鍾國亮
Kuo-Liang Chung
口試委員: 蔡文祥
Wen-Hsiang Tsai
鍾國亮
Kuo-Liang Chung
顏嗣鈞
Hsu-chun Yen
花凱龍
Kai-Lung Hua
張峻源
Chun-Yuan Chang
學位類別： 碩士
Master
系所名稱： 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 38
中文關鍵詞： 準確性 、跌倒偵測 、前景建構 、前景偵測 、系統單晶片 、電腦視覺
外文關鍵詞： Accuracy, Fall detection, Foreground construction, Foreground detection, System on chip (SoC), Vision computing
相關次數： 點閱：308  下載：0 
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  •  上一筆

    • 在本文中，我們提出了一種新穎而強大的基於視覺及系統單晶片(SoC)的系統作為感測器，有效地偵測年長者跌倒。我們所提出的方法包含五個步驟：初始光源穩定性確認，基於梯度差的前景偵測，基於型態學膨脹和多影格的前景建構，解決錯誤跌倒偵測問題，以及使用基於通用輸入/輸出的跌倒警告傳輸之跌倒偵測確認。實驗使用影片實測，證明與相關方法相比，我們所提出的方法擁有低功耗，低成本和高精度的優點。

    In this paper, we propose a novel and robust vision- and system on chip (SoC)-based system as a sensor to effectively detect falls for the elderly. The proposed method consists of five steps: the initial light stability confirmation, gradient difference-based foreground detection, dilation- and multi-frame-based foreground construction, solving the false fall detection problem, and fall detection determination with a general-purpose input/output based fall warning transmission. Based on the real test videos, the comprehensive experiments have justified the low-powered, low cost, and high accuracy merits of the proposed method when compared with the related methods.

    指導教授推薦書 I 論文口試委員審定書 II 中文摘要 III Abstract in English IV 誌謝 V Contents VI List of Figures VIII List of Tables X 1 Introduction 1 1.1 Related Work 1 1.2 Motivation 3 1.3 Contributions 4 2 The Vision­ and SoC­Based Fall Detection System Setting in the Home Room 5 3 The Proposed Fall Detection Method 7 3.1 Step 1: Initial Light Stability Confirmation 7 3.2 Step 2: Gradient Difference­Based Foreground Detection 9 3.3 Step 3: Dilation­ and Multi­Frame­Based Foreground Construction 10 3.4 Step 4: Solving the False Fall Detection Problem 12 3.5 Step 5: Fall Detection Determination 15 4 Experimental Results 17 4.1 Performance Comparison Metrics 19 4.2 Test Dataset 20 4.3 Performance Evaluation and Comparison 23 5 Conclusion 24

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