Tomè, D;
Monti, F;
Baroffio, L;
Bondi, L;
Tagliasacchi, M;
Tubaro, S;
(2016)
Deep Convolutional Neural Networks for pedestrian detection.
Signal Processing: Image Communication
, 47
pp. 482-489.
10.1016/j.image.2016.05.007.
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Abstract
Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics. Despite the significant improvements, pedestrian detection is still an open challenge that calls for more and more accurate algorithms. In the last few years, deep learning and in particular Convolutional Neural Networks emerged as the state of the art in terms of accuracy for a number of computer vision tasks such as image classification, object detection and segmentation, often outperforming the previous gold standards by a large margin. In this paper, we propose a pedestrian detection system based on deep learning, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline we propose an architecture that outperforms traditional methods, achieving a task accuracy close to that of state-of-the-art approaches, while requiring a low computational time. Finally, we tested the system on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a forerunner computational brain of future self-driving cars.
| Type: | Article |
|---|---|
| Title: | Deep Convolutional Neural Networks for pedestrian detection |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.image.2016.05.007 |
| Publisher version: | http://dx.doi.org/10.1016/j.image.2016.05.007 |
| Language: | English |
| Additional information: | Copyright © 2016. This manuscript version is published under a Creative Commons Attribution Non-commercial Non-derivative 4.0 International licence (CC BY-NC-ND 4.0). This licence allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licences are available at http://creativecommons.org/licenses/by/4.0. Access may be initially restricted by the publisher. |
| Keywords: | Deep learning; Pedestrian detection; Convolutional Neural Networks; Optimization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1502132 |
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