This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection in Intelligent Transportation Systems. The basic components of pedestrians are first located in the image and then combined with a support-vector-machine-based classifier. This poses the problem of pedestrian detection in real cluttered road images. Candidate pedestrians are located using a subtractive clustering attention mechanism based on stereo vision. A components-based learning approach is proposed in order to better deal with pedestrian variability, illumination conditions, partial occlusions, and rotations. Extensive comparisons have been carried out using different feature extraction methods as a key to image understanding in real traffic conditions. A database containing thousands of pedestrian samples extracted from real traffic images has been created for learning purposes at either daytime or nighttime. The results achieved to date show interesting conclusions that suggest a combination of feature extraction methods as an essential clue for enhanced detection performance.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Combination of feature extraction methods for SVM pedestrian detection


    Contributors:


    Publication date :

    2007


    Size :

    16 Seiten, 35 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





    Combination of Feature Extraction Methods for SVM Pedestrian Detection

    Alonso, I.P. / Llorca, D.F. / Sotelo, M.A. et al. | IEEE | 2007


    Pedestrian Detection Using SVM and Multi-Feature Combination

    Sotelo, M.A.l. / Parra, I. / Fernandez, D. et al. | IEEE | 2006


    Multi-channel Feature for Pedestrian Detection

    He, Zhixiang / Xu, Meihua / Guo, Aiying | Springer Verlag | 2017