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Author: search.filters.author.Ohnishi, NoboruSubject: search.filters.subject.vehicle detection

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    Vehicle detection and classification system based on virtual detection zone
    (2016-11-18)
    Seenouvong, Nilakorn
    ;
    Watchareeruetai, Ukrit
    ;
    Nuthong, Chaiwat
    ;
    Khongsomboon, Khamphong
    ;
    Ohnishi, Noboru
    This paper proposes a vehicle detection and classification system based on virtual detection zone (VDZ). The proposed system consists of four main steps: foreground extraction, vehicle detection, vehicle feature extraction and vehicle classification. A moving vehicle is firstly detected based on Gaussian mixture model (GMM). Then, several techniques including region of interest selection, adaptive morphological operation, and contour processing are applied to obtain correct foreground objects. Next, vehicle features are calculated when the centroid of a vehicle is on the VDZ. Finally, vehicles are classified by using k-nearest neighbor classifier. Experimental results show that the proposed method can accurately detect and classify vehicles with an accuracy of 98.53%.
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    A computer vision based vehicle detection and counting system
    (2016-03-23)
    Seenouvong, Nilakorn
    ;
    Watchareeruetai, Ukrit
    ;
    Nuthong, Chaiwat
    ;
    Khongsomboon, Khamphong
    ;
    Ohnishi, Noboru
    A vehicle detection and counting system plays an important role in an intelligent transportation system, especially for traffic management. This paper proposes a video-based method for vehicle detection and counting system based on computer vision technology. The proposed method uses background subtraction technique to find foreground objects in a video sequence. In order to detect moving vehicles more accurately, several computer vision techniques, including thresholding, hole filling and adaptive morphology operations, are then applied. Finally, vehicle counting is done by using a virtual detection zone. Experimental results show that the accuracy of the proposed vehicle counting system is around 96%.