Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks. / Dinakaran, Ranjith ; Easom, Philip ; Zhang, Li; Bouridane, Ahmed ; Jiang, Richard ; Edirisinghe, Eran.

2019 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN) : IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published
  • Ranjith Dinakaran
  • Philip Easom
  • Li Zhang
  • Ahmed Bouridane
  • Richard Jiang
  • Eran Edirisinghe

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 to generate random transformations of images with missing pixels to expand existing labelled datasets. In our work, GAN's 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 showed between DCGAN cascaded with SSD and SSD itself.
Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationInternational Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
ISBN (Electronic)978-1-7281-1985-4
ISBN (Print)978-1-7281-1986-1
DOIs
Publication statusPublished - 30 Sep 2019
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 43383194