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 (2019) Deep Learning based Pedestrian Detection at Distance in Smart Cities. In: Intellisys 2019 - Intelligent Systems Conference, 5th - 6th September 2019, London, UK. (In Press)

[img]
Preview
Text
Dinakaran et al - Deep Learning based Pedestrian Detection at Distance in Smart Cities AAM.pdf - Accepted Version

Download (548kB) | Preview

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: Conference or Workshop Item (Paper)
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
Related URLs:
Depositing User: Paul Burns
Date Deposited: 03 Jul 2019 11:22
Last Modified: 11 Oct 2019 09:35
URI: http://nrl.northumbria.ac.uk/id/eprint/39849

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence