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Proceedings Paper

Aggregated channels network for real-time pedestrian detection
Author(s): Farzin Ghorban; Javier Marín; Yu Su; Alessandro Colombo; Anton Kummert
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Paper Abstract

Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware. In order to alleviate this drawback, most strategies focus on using a two-stage cascade approach. Essentially, in the first stage a fast method generates a significant but reduced amount of high quality proposals that later, in the second stage, are evaluated by the CNN. In this work, we propose a novel detection pipeline that further benefits from the two-stage cascade strategy. More concretely, the enriched and subsequently compressed features used in the first stage are reused as the CNN input. As a consequence, a simpler network architecture, adapted for such small input sizes, allows to achieve real-time performance and obtain results close to the state-of-the-art while running significantly faster without the use of GPU. In particular, considering that the proposed pipeline runs in frame rate, the achieved performance is highly competitive. We furthermore demonstrate that the proposed pipeline on itself can serve as an effective proposal generator.

Paper Details

Date Published: 13 April 2018
PDF: 10 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960I (13 April 2018); doi: 10.1117/12.2309864
Show Author Affiliations
Farzin Ghorban, Univ. Wuppertal (Germany)
Delphi Deutschland (Germany)
Javier Marín, Massachusetts Institute of Technology (United States)
Yu Su, Delphi Deutschland (Germany)
Alessandro Colombo, Delphi Deutschland (Germany)
Anton Kummert, Univ. Wuppertal (Germany)

Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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