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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/7985


    Title: 以熵為特徵之視覺化交通監視系統;A Vision-based Traffic Monitoring System Using Entropy-based Features
    Authors: 許威烈;Wei-Lieh Hsu
    Contributors: 通訊工程研究所
    Keywords: ;交通監視系統;車輛追蹤;Entropy;Vehicle Tracking;Traffic Monitoring System
    Date: 2004-06-24
    Issue Date: 2009-09-22 11:15:08 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 摘 要 交通壅塞是世界各國共同之問題,由於增建之道路有限,交通建設緩不濟急,塞車情況日愈嚴重,不僅造成運輸機動性低落,也付出了相當之社會成本,同時衍生了不少問題:例如能量的消耗,環境的汙染等,解決之道,歐美各先進國家的莫不採用高科技之應用,整合通訊,控制,電子與資訊等技術為基礎之智慧型運輸系統(Intelligent Transportation System)來解決,使有限之運輸資源作最有效的利用,以提高人民生活品質及經濟競爭力。 然而ITS之有效運作,有賴於正確之即時交通參數,因此交通參數之擷取為ITS之首要工作,故交通監控系統之不斷被要求改進,以獲取快速有效之交通參數。傳統使用感應線圈,紅外線,微波等偵測器,來擷取交通參數,由於成本高,維護不易;而應用影像處理與電腦視覺等技術,不僅成本低,安裝容易,且可獲得較大的區域之量測,及較多樣之資訊,除擷取傳統的交通參數外,如車流量,車速等,進一步可偵測車陣佇列,及追蹤違規車輛及快速偵測交通事故等;所以應用影像處理與電腦視覺來作交通參數之擷取,是目前智慧型運輸系統之主要技術。然而影像處理技術易受週遭光線,陰影,及遮蔽等影響,增加了處理之困難度,也是極待克服之問題。 我們以電腦視覺及影像處理之技術,從巨觀之觀點,以熵的計量作為特徵值,提出一套交通監控系統,採用由觀測點之攝影機傳送來之影像,在待測之車道上,決定偵測區。由於偵測區內主動像素分布愈廣之區域,其熵之值愈大,藉由此特性,以熵為特徵值比較不受光線變化之影嚮,同時熵之計算速度快,符合即時處理之要求,並適用各種不同之天候下。 本研究包括三個主題:主題一:我們利用熵之量測,來擷取即時之交通參數,包含車輛數目,車流,車速,及其車陣佇列等。同時利用熵之特性,提出一套有效率之背景影像之更新法,不僅結果正確,並節省不少計算時間。 主題二:為達到正確追蹤車輛之功能,我們將二值化影像中之主動像素以一長方形環繞著,以車速及車輛之明顯之前緣,來估算車輛在下一像框之位置,再以熵的值來追蹤車輛移動之軌跡;並可迅速追蹤變換車道之車輛。主題三:由於交通事故造成社會成本極大之損失,我們將待測區每一車道分割成數個小區域,行成二維細胞模型,每小區域相對模型中之一細胞,再依連續兩張影像之熵值之變化,定義每一細胞之狀態,可將複雜之多車道交通情況以動態離散系統來表之,並訂出規則來描述此模型,當違反規則,此可視為異常之事件,藉此可快速之交通事故。 本研究經一序列之實驗證實其正確性高,不僅符合即時之要求,且可達到節省經費,提高效率之目的。同時以熵值為特徵之方法,較不受光線變化之影嚮,可適用各種不同之天候下。 ABSTRACT This dissertation discusses three major issues that are related to traffic monitoring. The underlying features used in this work are adopted all based on the entropy measurement. Entropy is a commonly measurement that can be adopted to describe the degree of disorder in thermodynamics. It is worth noting that a detection zone that has more blocks containing active pixels, its corresponding entropy will be relatively high, even though the amount of actives pixels is small. Based on this useful property, entropy-based features tolerate a wide range selection of the threshold value. Since the detection of entropy is immune to the effect of lighting condition, it can accommodate to different weather conditions, such as sunny days, cloudy days and rainy days. The first topic discussed in this dissertation is about traffic parameter extraction. Based on an entropy measurement, a number of important traffic parameters, such as traffic flow, space mean speed and traffic queue length, can be determined in real time. On the other hand, since the accurate computation of entropy measurement depends on the distribution of active pixels, thus an efficient background updating is always required at a certain time. In order to obtain the accurate traffic parameters, we also propose a better background updating method. Instead of updating the whole background image, we must update the area of interest. As a result, the entropy value of the area of interest must be calculated frame by frame. Under these circumstances, the proposed method can save a lot of computing time and it makes some real-time applications possible. The second topic discussed in this dissertation is to develop a new method for real-time vehicle detection and tracking. Based on a number of features, we propose a macroscopic method which is able to perform real-time tracking of moving vehicles on highway. In addition to tracking a normal car running on highway, the proposed method can also track a vehicle performing lane change. This approach consists of two phases: a detection phase and a tracking phase. In the detection phase, we use entropy-based features and the salient edges of a vehicle to check for the existence of vehicles. Then, we use a bounding box to track the targeted vehicle, and use its velocity to estimate the possible location at the next image frame. The, we perform the tracking task based on the extracted entropy features to determine the accurate location of the target vehicle. By conducting a great number of experiments, the et number of experiments, the experimental results demonstrated that the proposed system is useful. This third issue covered in this dissertation is with regard to the automatic detection of traffic accident. We propose a 2D cellular model based on entropy-based features to describe the dynamic behavior of the freeway traffic. Using the proposed model, the complex traffic of multilane can be easily represented by a dynamic discrete system. Based on a number of important information derived from the above mentioned system, traffic incidents can be easily detected. From the experimental results, we demonstrated the efficiency as well as the effectiveness of the proposed system.
    Appears in Collections:[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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