Deep Learning based Pedestrian Detection at Distance in Smart Cities

Dinakaran, Ranjith, Easom, Philip, Bouridane, Ahmed, Zhang, Li, Jiang, Richard, Mehboob, Fozia and Rauf, Abdul (2020) Deep Learning based Pedestrian Detection at Distance in Smart Cities. In: Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys). Volume 2. Advances in Intelligent Systems and Computing (1038). Springer, Cham, pp. 588-593. ISBN 9783030295127, 9783030295134

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Dinakaran et al - Deep Learning based Pedestrian Detection at Distance in Smart Cities AAM.pdf - Accepted Version

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Official URL: https://doi.org/10.1007/978-3-030-29513-4_43

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.

Item Type: Book Section
Uncontrolled Keywords: Deep Neural Networks, Object Detection, Smart Homecare, Smart Cities
Subjects: G400 Computer Science
K900 Others in Architecture, Building and Planning
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 03 Jul 2019 11:22
Last Modified: 31 Jul 2021 18:18
URI: http://nrl.northumbria.ac.uk/id/eprint/39849

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