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|Other Titles: ||The study of the motorcycle detection algorithm|
|Authors: ||莊劍嵐;Chuang, Jian-lan|
|Keywords: ||機車偵測;遮蔽偵測;區塊切割;車輛種類;交通流量;Motorcycle Detection;Occlusion Detection;Block Segmentation;Vehicle Classification;Traffic flow|
|Issue Date: ||2010-01-11 04:34:54 (UTC+8)|
|Abstract: ||近年來由於伴隨著ITS的蓬勃發展，影像處理技術也日趨受到重視，雖然在技術方面有大幅度的進步，但是仍未達到成熟的境界。而綜觀國內外學者之研究，多著重於小型車之辨識與偵測，對於機車之辨識偵測皆未有專門的探討，這除了由於國外交通情況不同，也與機車影像本身的特性有很大的關連。本研究首先針對機車影像之特性做一番探討：1、機車動線紊亂; 2、機車外形及騎士影像顯現複雜; 3、遮蔽問題嚴重。並針對其特性逐步構建機車偵測演算法。|
In recent years, with the rapid development of ITS, the image processing technique is being paid more attention day by day. However, the traffic situation differs from Taiwan and foreign countries, so does motorcycle image characteristics itself. As results of that, the passing relevant researches were more focused on detecting car image, not motorcycle image.
In this research, we will analyze the characteristics of the motorcycle image：1.The motorcycle moves confusedly, 2.Motorcycle appearance and driver’s image appear complicatedly, 3.Serious occlusion, in order to develop the “motorcycle detection algorithm”.
”Motorcycle detection algorithm” is different from the studies in past researches taking “one vehicle” as unit of detection, it takes “bunch” as a unit of detection. A “bunch” means the connecting blobs enter and leave detection zone. “Motorcycle detection algorithm” can be divided into three parts, “image preprocessing”、”blob relation processing” and “block relation processing”.
First, “image preprocessing” mainly utilizes the background subtraction and threshold to label and record blob’s positions.
Secondly, “blob relation processing” is depended on the blob of the center-of-gravity position and common factor relations between each two images in succession, going to judge whether or not it is the same blob. Then we take “bunch” as a unit of detection, dealing with every “bunch”.
Finally, we utilize the “block relation processing” to carry on block to block segmentation match etc, to separate the specific vehicle.
In our study, we have successfully extracted the traffic parameters which are included vehicle classification and traffic flow. After our experimental analysis, the rate of accurately identifying small vehicles is 87.61 %, and 93.16 % for motorcycles. Under the occlusion situations, the accurate rate of identifying small vehicle is 85.89 %, and 89.59 % for motorcycles.
|Appears in Collections:||[運輸管理學系暨研究所] 學位論文|
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