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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/735


    Title: 以卷積神經網路演算法為基礎之自動化車流影像辨識技術;Automated Traffic Image Identification Technology Based on Convolutional Neural Network Algorithm
    Authors: 蔡曉佩;TSAI, HSIAO-PEI
    Contributors: 雲端計算與物聯網數位學習碩士在職專班
    Keywords: 車流量計數;車輛影像辨識;卷積神經網路;Traffic flow counting;Vehicle Image Recognition;Convolutional Neural Network
    Date: 2017
    Issue Date: 2019-07-17 10:44:59 (UTC+8)
    Publisher: 雲端計算與物聯網數位學習碩士在職專班
    Abstract: 由於臺灣地區經濟快速成長,運輸路網發達、車輛普及化,進而衍生許多交通相關問題。根據統計資料顯示,台灣地區人口已達2355.2萬人,另汽機車登記數量共計2160.4萬輛,平均每1人就擁有一部汽機車,造成汽機車數量過大而導致許多民生問題,包括交通擁塞、都會區車輛排氣污染等,更成政府施政的重點之一。因此車流量的估測與計算是一個很重要之研究因素,我們可以透過車流量區分路段的尖峰與離峰時間,進而以替代道路或是調撥車道等其他方式紓解交通擁塞的問題;因應車輛普及化,車子排放的廢氣也造成空氣汙染等環境議題,亦可透過車流種類分類運用至運輸交通影響空汙研究調查,進而探討影響該區域空氣汙染的原因,是否是行經的特定車種所致,亦或是純粹車流過大等問題。本研究係以影像處理為基礎,透過攝影機拍攝臺中市具代表性或指標性道路之路段或路口,透過影像辨識技術進行車流量調查工作,作為後續之先導研究。首先於路口架設攝影機,並透過網路串流的方式傳回取得車流的影像畫面,本研究提出以電腦視覺與影像分析的自動化系統進行畫面前後景分離,達到前景物件獨立並給予數量編號,以進行車流量計數;近年來快速發展的深度學習技術可以大幅增強影像辨識的能力,因此本文亦加入卷積神經網路演算法,這是最常見的深度學習網路架構之一,讓系統依特徵抽取方式,對車輛大小及種類進行自動分類辨識。由於目前物聯網技術的快速發展,加上路口及交通監視器的廣泛普及,利用攝影機監測交通狀況,並搭配自動影像處理分析進行即時交通資訊萃取,將可大幅度的省下許多硬體設備的花費,提升交通相關問題的處理綜效,所得的資料並可進行大資料分析,極具其他後續研究價值。
    The high growth of economy and transportation network in Taiwan introduces a very high density of vehicles. The statistics has revealed Taiwan's population is now 23.55 million, while the number of automobiles and motorcycles is more than 21.6 million. It means an average of 0.917 vehicles per person. These enormous amounts of vehicles have caused traffic congestion, vehicle exhaust pollution and many other problems in the urban areas, which quickly became the top priority issues to the government nowadays. Therefore, the estimation and calculation of traffic flow are the important research factors. We can distinguish the peak and off-peak time of the traffic through the traffic flow, and then alleviate the traffic congestion in alternative routes or other ways. The density of vehicles and exhaust emissions have also caused the air pollution and the other environmental issues. The reasons of the air pollution in the specific regions could also be explored by the classification of the different traffic flow and investigations on pollution effects of transportation. Thus, we may find if there is specific vehicle that will cause the severe pollution or just the heavy traffic flow lead to the results.The project is based on image processing over video data collected from traffic cameras in different road sections or junctions in Taichung City. Through the image analyzing system developed in this study, the automatic counting and categorizing of the traffic are then processed. Firstly, the camera was set up at the intersections and the images of the traffic flow were transmitted through the network. The computer vision and image analysis was then performed to separate the foreground objects from the background. An item number was assigned to each foreground object found and number of cars counted. To further enhance the capability of the image analysis, the convolution neural network algorithm was incorporated. With the power of the deep learning, the system can automatically identify the type of vehicles and properly categorize them.With the rapid development of the IoT technology and the wide spread of the traffic camera, the capability to automatically extract vehicle information from the video is extremely invaluable. These data can serve as the foundation to help solving the traffic related problems in a very cost-effective way. Furthermore, their use in the follow up big data research will be significant.
    Appears in Collections:[雲端計算與物聯網數位學習碩士班] 學位論文

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