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

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

Research output: Contribution to conferencePaperpeer-review

15 Downloads (Pure)

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’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
Number of pages6
Publication statusPublished - 14 Jul 2019
Event2019 International Joint Conference on Neural Networks - InterContinental Budapest Hotel, Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019
https://www.ijcnn.org/

Conference

Conference2019 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2019
CountryHungary
CityBudapest
Period14/07/1919/07/19
Internet address

Fingerprint Dive into the research topics of 'Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks'. Together they form a unique fingerprint.

Cite this