Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks

TL;DR

This paper explores using Deep Convolutional GANs combined with Single Shot Detector to improve pedestrian detection in complex, real-world environments by augmenting datasets with generated image variations.

Contribution

It introduces a novel approach of integrating DCGANs with SSD for data augmentation, enhancing pedestrian detection accuracy in challenging scenarios.

Findings

GAN-augmented training improves detection performance

Effective in-fill image completion expands dataset diversity

Method performs well across multiple datasets and resolutions

Abstract

In this work, we examine the feasibility of applying Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) as data-processing technique to handle with the challenge of pedestrian detection in the wild. Specifically, we attempted to use in-fill completion (where a portion of the image is masked) to generate random transformations of images with portions missing to expand existing labelled datasets. In our work, GAN has been trained intensively on low resolution images, in order to neutralize the challenges of the pedestrian detection in the wild, and considered humans, and few other classes for detection in smart cities. The object detector experiment performed by training GAN model along with SSD provided a substantial improvement in the results. This approach presents a very interesting overview in the current state of art on GAN networks for object detection. We used Canadian Institute for Advanced Research (CIFAR), Caltech, KITTI data set for training and testing the network under different resolutions and the experimental results with comparison been showedbetween DCGAN cascaded with SSD and SSD itself.