We describe a real-time pedestrian detection system intended for use in automotive applications. Our system demonstrates superior detection performance when compared to many state-of-the-art detectors and is able to run at a speed of 14 fps on an Intel Core i7 computer when applied to 640×480 images. Our approach uses an analysis of geometric constraints to efficiently search feature pyramids and increases detection accuracy by using a multiresolution representation of a pedestrian model to detect small pixel-sized pedestrians normally missed by a single representation approach. We have evaluated our system on the Caltech Pedestrian benchmark which is currently the largest publicly available pedestrian dataset at the time of this publication. Our system shows a detection rate of 61% with 1 false positive per image (FPPI) whereas recent other state-of-the-art detectors show a detection rate of 50% ∼ 61% under the ‘reasonable’ test scenario (explained later). Furthermore, we also demonstrate the practicality of our system by conducting a series of use case experiments on selected videos of Caltech dataset.


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    Titel :

    Real-time pedestrian detection with deformable part models


    Beteiligte:
    Cho, Hyunggi (Autor:in) / Rybski, Paul E. (Autor:in) / Bar-Hillel, Aharon (Autor:in) / Zhang, Wende (Autor:in)


    Erscheinungsdatum :

    2012-06-01


    Format / Umfang :

    2027266 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real-Time Pedestrian Detection with Deformable Part Models

    Cho, H. / Rybski, P. / Bar Hillel, A. et al. | British Library Conference Proceedings | 2012


    Toward Real-Time Pedestrian Detection Based on a Deformable Template Model

    Pedersoli, Marco / Gonzalez, Jordi / Hu, Xu et al. | IEEE | 2014


    Integrating Multiscale Deformable Part Models and Convolutional Networks for Pedestrian Detection

    Chen, Wen-Hui / Kuan, Chi-Wei / Chiang, Chuan-Cho | TIBKAT | 2020