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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dinakaran, R., et al.: Image resolution impact analysis on pedestrian detection in smart cities surveillance. In: Proceedings of the 1st International Conference on Internet of Things & Ma-chine Learning (IML 2017) (2017)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal net-works. In: NIPS (2015)
Dosovitskiy, A., et al.; Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 99 (2015)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: ECCV (2016)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., WardeFarley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS (2014)
Nguyen, A., et al.: Plug & play generative networks: conditional iterative generation of images in latent space. In: CVPR (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
Denton, E.L., et al.: Deep generative image models using a laplacian pyramid of adversarial networks. In: NIPS, vol. 2, pp. 1486–1494 (2015)
Ledig, C., et al.: Photo-realistic single image super resolution using a generative adversarial network. In: CVPR open access version by computer vision foundation, CVPR (2017)
Chen, X., et al.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems (NIPS 2016), vol. 29 (2019)
Finn, C., et al.: Unsupervised learning for physical interaction through video prediction. In: Advances in Neural information Processing Systems, NIPS, vol. 29 (2016)
Mathieu, M., et al.: Deep multi-scale video prediction beyond mean square error. In: ICLR (2016)
Storey, G., Jiang, R., Bouridane, A.: Role for 2D image generated 3D face models in the rehabilitation of facial palsy. IET Healthcare Technology Letters (2017)
Storey, G., Bouridane, A., Jiang, R.: Integrated deep model for face detection and landmark localisation from in the wild image, IEEE Access (in press)
Jiang, R., Ho, A.T., Cheheb, I., Al-Maadeed, N., Al-Maadeed, S., Bouridane, A.: Emotion recognition from scrambled facial images via many graph embedding. Pattern Recogn. 67, 245–251 (2017)
Jiang, R., Al-Maadeed, S., Bouridane, A., Crookes, D., Celebi, M.E.: Face recognition in the scrambled domain via salience-aware ensembles of many kernels. IEEE Trans. Inf. Forensics Secur. 11(8), 1807–1817 (2016)
Jiang, R., Bouridane, A., Crookes, D., Celebi, M.E., Wei, H.L.: Privacy-protected facial biometric verification via fuzzy forest learning. IEEE Trans. Fuzzy Syst. 24(4), 779–790 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Dinakaran, R.K. et al. (2020). Deep Learning Based Pedestrian Detection at Distance in Smart Cities. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-29513-4_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29512-7
Online ISBN: 978-3-030-29513-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)