基於遞迴神經網路於多重深度攝影機架構下之駕駛動作辨識

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姓名

莊英瑋(Ying-Wei Chuang) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於遞迴神經網路於多重深度攝影機架構下之駕駛動作辨識
(Driver Behavior Recognition based on Multiple Depth Cameras using Recurrent Neural Network)

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摘要(中)

本篇論文是針對車內駕駛的動作辨識，針對駕駛動作的目的，一方面是和行車安全有高度相關性，在發現駕駛不專心時或有危險時給予提醒，另一方面可應用在車上型娛樂的控制上。我們提出利用兩台的Kinect攝影機，拍攝到的不同視角影像、經過前處理，並利用深度學習裡面的遞迴神經網路架構去做訓練辨識。使用不同視角的影像降低只用單一視角造成的自我遮蔽的問題，使用長短期記憶的架構可以讓網路學習到隨時間變化而改變的資訊，這套系統應用在我們自己拍攝的Vap多視角駕駛動作資料庫上，可以達到不錯的辨識正確率

摘要(英)

This thesis is aimed at in-car driver behavior recognition. One of the purpose is for the safe drive, because it would be dangerous that driver doesn’t concentrate when driving. The other is the application for the In-car entertainment. We propose a multi-view driver behavior recognition system (MDBR system). The pointcloud is captured from different views, and we manage to preprocess the original data by rotation, calibration, merging and sampling. Then, we use the Long short-term memory (LSTM) network, a type of recurrent neural network, as classifier. The dataset we used is VAP multi-view driver behavior dataset. This dataset is we proposed, and contain 10 driver behavior. Using multi-view data can effectively reduce the influence of the occlusion problem. The recognition accuracy of MDBR system have good performance.

關鍵字(中)

★ 駕駛動作辨識
★ 深度攝影機
★ 深度學習
★ 多視角拍攝

關鍵字(英)

★ driver behavior recognition
★ depth camera
★ deep learning
★ RNN

論文目次

摘要 I
Abstract II
誌謝 III
目錄 V
圖目錄 VII
表目錄 X
第一章緒論 1
1.1 研究背景 1
1.2　研究動機與目的 2
1.3　論文架構 4
第二章　深度攝影機及動作辨識相關介紹 5
2.1　深度攝影機 5
2.1.1　Kinect深度攝影機 5
2.1.2　硬體規格 6
2.1.3 技術與功能 8
2.1.4 開發工具介紹 Kinect SDK 14
2.2 動作辨識 15
2.2.1 動作辨識相關文獻介紹 16
2.2.2 車內行為辨識 19
第三章　深度學習相關基本介紹 21
3.1　類神經網路 21
3.1.1 生物神經元 22
3.1.2 人工神經元 23
3.1.3人工神經網路 28
3.2　深度學習 31
3.2.1 深度神經網路 31
3.2.2 遞迴神經網路 33
3.2.3 長短期記憶 (LSTM) 35
第四章提出之車內駕駛動作辨識系統 37
4.1　系統架構 37
4.2　利用骨架當作特徵進行駕駛動作辨識 39
4.3 利用多視角點雲當作特徵進行駕駛動作辨識 41
4.4　VAP多視角駕駛動作資料庫 44
第五章實驗結果與分析討論 49
5.1　實驗環境介紹 49
5.2　實驗結果 50
5.2.1 骨架特徵輸入之實驗結果 50
5.2.2 多視角點雲特徵輸入之實驗結果 55
5.3　比較與討論 64
第六章　結論與未來展望 74
參考文獻 75

參考文獻

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指導教授

張寶基(Pao-Chi Chang)

審核日期

2018-8-2

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