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    Vulnerable Road Users Detection using Convolutional Deep Feedforward Network

    Lau M 2021.pdf (4.357Mb)
    Access Status
    Open access
    Authors
    Lau, Mian Mian
    Date
    2021
    Supervisor
    Zhuquan Zang
    Hann Lim
    Type
    Thesis
    Award
    PhD
    
    Metadata
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    Faculty
    Curtin Malaysia
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/83745
    Collection
    • Curtin Theses
    Abstract

    A new convolutional deep feedforward network (C-DFN) is proposed to detect vulnerable road users at 57.9% misclassification rate using Caltech Dataset. Instead of going deeper, three C-DFN is stacked to achieve 43.4% misclassification rate. Part-based C-DFN further reduces the rate of 42.5% to tackle occlusion problem. In addition, investigation of adaptive activation functions are performed to understand the effect of saturated and non-saturated functions in mitigating the vanishing and exploding gradient issues of neural networks.

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