Deep learning approaches have improved pedestrian identification by employing neural networks to routinely study and excerpt features from complex visual data. The application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) has shown promising results in improving the precision of pedestrian detection systems. These developments improve traffic safety to make a substantial contribution to the creation of effective monitoring systems. Pedestrian detection technology is a useful tool that smart city infrastructure can use to optimize pedestrian flow, manage traffic, and increase public safety. Future pedestrian detection researchers will find great value in having a thorough understanding of the methodologies used in earlier studies. Researchers can further develop and create deep learning models by expanding upon the foundations set by previous studies. This work will help to further improve autonomous vehicles, intelligent transportation systems, and the general growth of smart cities.


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    Title :

    A Review on the Effectiveness of Deep Learning Algorithms for Pedestrian Detection


    Contributors:


    Publication date :

    2024-11-06


    Size :

    415881 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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