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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/404


    Title: 基於光流及深度學習的超車偵測技術;Overtaking Vehicle Detection Techniques Based on Optical Flow and Deep Learning
    Authors: 吳律廷;WU, LU-TING
    Contributors: 電機工程研究所
    Keywords: 光流法;卷積類神經網路;超車偵測;Optical Flow;Convolutional Neural Network;Overtaking Vehicle Detection
    Date: 2017
    Issue Date: 2019-07-17
    Publisher: 電機工程研究所
    Abstract: 近年來,智慧型車輛的興起,讓車用影像成為熱門的研究項目,本研究提出一個利用安裝於車輛正後方的行車紀錄器偵測超車並協助駕駛變換車道的即時系統。在這項工作中,我們提出基於運動線索定位出可能的目標物。接著,用卷積類神經網路(Convolutional Neural Network)辨識車輛,並用一段時間的追蹤判斷車輛的行為,確認為超車之後才對駕駛發出警告。此外,我們針對運動線索和卷積類神經網路同時在重複物件問題上發生的錯誤提出一個可靠的算法,我們實驗於白天的市區、高速公路以及夜晚的場景中,總計共有180,000個幀,每一張幀所需時間約為10ms,這證實此系統能良好的使用於真實的道路上。
    In recent years, the rise of intelligent vehicles makes the car images become popular research issues. This thesis proposes a real-time system that uses a monocular camera mounted on the rear of a vehicle to detect overtaking. In this work, we propose to locate vehicles based on motion cues, then identify the vehicle with the Convolutional Neural Network(CNN) and track the behavior of the vehicle for a period of time. In addition, we propose a reliable algorithm for the issue of repetitive patterns. Experimental results are presented with real scene video sequences. The performance evaluation has demonstrated the effectiveness of the proposed techniques.
    Appears in Collections:[電機工程研究所] 學位論文

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