Big data leads to a great success of deep learning in computer vision. Unfortunately, big datasets are often not balanced in all dimensions and rare cases are often underrepresented. On-board data collection by a moving vehicle can capture thousands of normal pedestrians and vehicles, but what about special persons like police officers, road workers, and school guards? Not only that those types of classes are hard to get, they are crucial to be recognized and classified as such for the task of automated driving. Future self-driving cars need to interact with their environment and need to also understand and follow the signals and instructions of those special persons. In this paper, we show how to classify special person types using Convolutional Neural Networks. The big data imbalance is handled by data augmentation using Generative Models, showing a clear advantage over classical data augmentation. The classification performance of special persons can be significantly improved using our Generative Model based Data Augmentation.


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    Title :

    Generative Model based Data Augmentation for Special Person Classification


    Contributors:
    Guo, Zijie (author) / Zhi, Rong (author) / Zhang, Wuqaing (author) / Wang, Baofeng (author) / Fang, Zhijie (author) / Kaiser, Vitali (author) / Wiederer, Julian (author) / Flohr, Fabian (author)


    Publication date :

    2020-10-19


    Size :

    2125094 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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