以人體姿態識別ATM提款動作之研究

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姓名

鄧俊祥(Jun-Xiang Deng) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

以人體姿態識別ATM提款動作之研究
(Research on ATM Withdrawal Action by Human Activity Recognition)

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至系統瀏覽論文 (2027-3-14以後開放)

摘要(中)

人體姿態識別在近年來廣泛的應用在醫療、運動、嬰兒老人監控以及犯罪監視等等，而在目前人體姿態識別上較多人使用的為 Openpose，因為它有著簡易的特性，不需要高階攝像頭，以普通 2D 的 RGB 圖像即可達成關節點估計，不僅僅可以偵測身體支點，同時也可以偵測手部以及臉部關節點。本研究將此運用於車手和非車手的 ATM 提領影片中，再透過和專業警員的訪談，得出車手在 ATM 提領時的動作特徵，分別為插卡、看手機、左顧右盼、取錢和清點等等，然而將影片所得出之座標點轉換為特徵值，以特徵值的方式去描述 ATM 提領的動作特徵，又因所述動作特徵在影片裡佔比偏低，資料有不平衡情況，所以本研究透過 n gram 以及 undersampling 的方式，利用深度學習模型雙向 LSTM(Long Short-term memory 長短期記憶模型)進行判斷，以追求較高的精確率和召回率。

摘要(英)

In recent years, human activity recognition(HAR) has been widely used in health-care, sports, baby and elderly monitoring and crime surveillance, etc. At present, Openpose is used by most people in HAR, because it has simple characteristics and does not A high-level camera is needed, and the joint point estimation can be achieved with ordinary 2D RGB images. It can not only detect the body joint, but also the hand and face joint points. Our study applies Openpose to the ATM withdrawal videos of moneymule and non-moneymule, and then through interviews with professional police officers, we can get the characteristics of the driver′s actions when withdrawing from the ATM. It including inserting a card, looking at the phone, looking around, and withdrawing money,and counting, etc. However, we convert the coordinate points obtained from the film into feature, and describe the action characteristics of ATM withdrawal in the form of feature. Also, because the action features occupy a low proportion in the video, the data is imbalance, so we use the deep learning model, bi-LSTM (Long Short-term memory model) to make evaluate in the way of n gram and undersampling, in order to pursue a higher precision and recall rate.

關鍵字(中)

★ 車手
★ openpose
★ lstm
★ atm

關鍵字(英)

★ money mule
★ openpose
★ lstm
★ atm

論文目次

摘要 ii
Abstract iii
目錄 iv
圖目錄 vii
表目錄 ix
第1章緒論 1
1-1 研究背景 1
1-2 研究動機 4
1-3 研究目的 5
1-4 研究架構 6
第2章文獻探討 7
2-1 OPENPOSE人體姿態識別 7
2-2 利用LSTM對動作進行判斷 8
2-3 ATM影片研究 9
第3章研究方法 10
3-1 研究流程 10
3-2 OPENPOSE人體姿態識別 11
3-3 車手動作 13
3-4 特徵值 15
3-5 LONG SHORT-TERM MEMORY長短期記憶模型 20
3-6 評估模型 26
第4章研究實驗 28
4-1 資料蒐集 28
4-2 資料預處理 29
4-2-1 影片挑選 29
4-2-2影片擷取 29
4-2-3利用Openpose將關節點轉換為座標點 30
4-2-4標記動作 31
4-2-5轉換為特徵值 31
4-2-6 N gram 32
4-2-7 Undersampling 33
4-3 LSTM實驗過程 34
4-4 實驗結果比較 39
4-3-1 插卡 40
4-3-2 左顧右盼 44
4-3-3 清點 48
4-5 小結 54
第5章結論 56
5-1 研究結論 56
5-2 研究限制以及未來研究 57
參考文獻 58

參考文獻

參考文獻
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指導教授

許秉瑜沈國基(Ping-Yu Hsu Guy-Ji Sheen)

審核日期

2022-3-14

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