A monocular vision based detection algorithm is presented to detect rear vehicles. Our detection algorithm consist of two main steps: knowledge based hypothesis generation and appearance based hypothesis verification. In the hypothesis generation step, a shadow extraction method is proposed based on contrast sensitivity to extract regions of interest (ROI), it can effectively solve the problems caused by casting shadow and illuminations. In the hypothesis verification step, one improved wavelet feature extraction approach based on HSV space was proposed. Moreover, in order to satisfy different application requirements, a new method based on probability density function is proposed to decide the decision boundary for Support Vector Machine. The algorithm was tested under various traffic scenes at different daytime, the result illustrated good performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A monocular-vision rear vehicle detection algorithm


    Contributors:
    Wei Liu, (author) / Chunyan Song, (author) / XueZhi Wen, (author) / Huai Yuan, (author) / Hong Zhao, (author)


    Publication date :

    2007-12-01


    Size :

    1350485 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicle distance detection based on monocular vision

    Bao, Dongsheng / Wang, Peikang | IEEE | 2016


    Moving vehicle detection and tracking using monocular vision

    Miao,X. / Li,S. / Shen,H. et al. | Automotive engineering | 2014



    Moving vehicle detection and tracking using monocular vision

    Miao, Xiaoding / Li, Shunming / Shen, Huan et al. | Tema Archive | 2014


    Vehicle Distance Detection Using Monocular Vision and Machine Learning

    Hanna, Maryam Samir Naguib Girgis | BASE | 2019

    Free access