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;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.
