PedVis-VGG-16: A Fine-tuned deep convolutional neural network for pedestrian image classifications

Abstract

Recently, pedestrian detection has attracted a lot of attention in recent years. It is known as a computer vision research hotspot, widely used in different fields. Despite the impressive progress of its approaches, their performance remains unsatisfactory. This paper proposes PedVis-Vgg-16a deep learning network for automatically detecting pedestrians presence in visible images. The suggested architecture is based on the fine-tuned VGG-16 architecture with modifications to the last block of the model. Different improvement components including data augmentation, parameter optimization, and parameter adaption, were taken to enhance the architecture performance. The newly designed architecture is validated on the publicly available dataset INRIA, which contains 4001 images and the results provided are satisfactory.