Standard
Deep learning based pedestrian detection at distance in smart cities. / Dinakaran, Ranjith K.; Easom, Philip; Bouridane, Ahmed et al.
Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2. ed. / Yaxin Bi; Rahul Bhatia; Supriya Kapoor. Springer-Verlag, 2020. p. 588-593 (Advances in Intelligent Systems and Computing; Vol. 1038).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Dinakaran, RK, Easom, P, Bouridane, A, Zhang, L
, Jiang, R, Mehboob, F & Rauf, A 2020,
Deep learning based pedestrian detection at distance in smart cities. in Y Bi, R Bhatia & S Kapoor (eds),
Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2. Advances in Intelligent Systems and Computing, vol. 1038, Springer-Verlag, pp. 588-593, Intelligent Systems Conference, IntelliSys 2019, London, United Kingdom,
5/09/19.
https://doi.org/10.1007/978-3-030-29513-4_43APA
Dinakaran, R. K., Easom, P., Bouridane, A., Zhang, L.
, Jiang, R., Mehboob, F., & Rauf, A. (2020).
Deep learning based pedestrian detection at distance in smart cities. In Y. Bi, R. Bhatia, & S. Kapoor (Eds.),
Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2 (pp. 588-593). (Advances in Intelligent Systems and Computing; Vol. 1038). Springer-Verlag.
https://doi.org/10.1007/978-3-030-29513-4_43Vancouver
Dinakaran RK, Easom P, Bouridane A, Zhang L
, Jiang R, Mehboob F et al.
Deep learning based pedestrian detection at distance in smart cities. In Bi Y, Bhatia R, Kapoor S, editors, Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2. Springer-Verlag. 2020. p. 588-593. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-29513-4_43
Author
Dinakaran, Ranjith K. ; Easom, Philip ; Bouridane, Ahmed et al. /
Deep learning based pedestrian detection at distance in smart cities. Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2. editor / Yaxin Bi ; Rahul Bhatia ; Supriya Kapoor. Springer-Verlag, 2020. pp. 588-593 (Advances in Intelligent Systems and Computing).
Bibtex
@inproceedings{b9d8b9f5aadf45b6beed39444c3108f8,
title = "Deep learning based pedestrian detection at distance in smart cities",
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.",
keywords = "Deep neural networks, Object detection, Smart cities, Smart homecare",
author = "Dinakaran, {Ranjith K.} and Philip Easom and Ahmed Bouridane and Li Zhang and Richard Jiang and Fozia Mehboob and Abdul Rauf",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-29513-4_43",
language = "English",
isbn = "9783030295127",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer-Verlag",
pages = "588--593",
editor = "Yaxin Bi and Rahul Bhatia and Supriya Kapoor",
booktitle = "Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2",
note = "Intelligent Systems Conference, IntelliSys 2019 ; Conference date: 05-09-2019 Through 06-09-2019",
}
RIS
TY - GEN
T1 - Deep learning based pedestrian detection at distance in smart cities
AU - Dinakaran, Ranjith K.
AU - Easom, Philip
AU - Bouridane, Ahmed
AU - Zhang, Li
AU - Jiang, Richard
AU - Mehboob, Fozia
AU - Rauf, Abdul
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Deep neural networks
KW - Object detection
KW - Smart cities
KW - Smart homecare
U2 - 10.1007/978-3-030-29513-4_43
DO - 10.1007/978-3-030-29513-4_43
M3 - Conference contribution/Paper
AN - SCOPUS:85072835032
SN - 9783030295127
T3 - Advances in Intelligent Systems and Computing
SP - 588
EP - 593
BT - Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2
A2 - Bi, Yaxin
A2 - Bhatia, Rahul
A2 - Kapoor, Supriya
PB - Springer-Verlag
T2 - Intelligent Systems Conference, IntelliSys 2019
Y2 - 5 September 2019 through 6 September 2019
ER -
