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  • IntelliSys2019_Ranjith

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Deep learning based pedestrian detection at distance in smart cities

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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  • Ranjith K. Dinakaran
  • Philip Easom
  • Ahmed Bouridane
  • Li Zhang
  • Richard Jiang
  • Fozia Mehboob
  • Abdul Rauf
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Publication date1/01/2020
Host publicationIntelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2
EditorsYaxin Bi, Rahul Bhatia, Supriya Kapoor
PublisherSpringer-Verlag
Pages588-593
Number of pages6
ISBN (Print)9783030295127
<mark>Original language</mark>English
EventIntelligent Systems Conference, IntelliSys 2019 - London, United Kingdom
Duration: 5/09/20196/09/2019

Conference

ConferenceIntelligent Systems Conference, IntelliSys 2019
Country/TerritoryUnited Kingdom
CityLondon
Period5/09/196/09/19

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1038
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceIntelligent Systems Conference, IntelliSys 2019
Country/TerritoryUnited Kingdom
CityLondon
Period5/09/196/09/19

Abstract

Generative adversarial networks (GANs) have been promising for many computer vision problems due to their powerful capabilities to enhance the data for training and test. In this paper, we leveraged GANs and proposed a new architecture with a cascaded Single Shot Detector (SSD) for pedestrian detection at distance, which is yet a challenge due to the varied sizes of pedestrians in videos at distance. To overcome the low-resolution issues in pedestrian detection at distance, DCGAN is employed to improve the resolution first to reconstruct more discriminative features for a SSD to detect objects in images or videos. A crucial advantage of our method is that it learns a multi-scale metric to distinguish multiple objects at different distances under one image, while DCGAN serves as an encoder-decoder platform to generate parts of an image that contain better discriminative information. To measure the effectiveness of our proposed method, experiments were carried out on the Canadian Institute for Advanced Research (CIFAR) dataset, and it was demonstrated that the proposed new architecture achieved a much better detection rate, particularly on vehicles and pedestrians at distance, making it highly suitable for smart cities applications that need to discover key objects or pedestrians at distance.