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Bio-inspired heterogeneous architecture for real-time pedestrian detection applications

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Abstract

Along with the development of powerful processing platforms, heterogeneous architectures are nowadays permitting new design space explorations. In this paper, we propose a novel heterogeneous architecture for reliable pedestrian detection applications. It deploys an efficient Histogram of Oriented Gradient pipeline tightly coupled with a neuro-inspired spatio-temporal filter. By relying on hardware–software co-design principles, our architecture is capable of processing video sequences from real-word dynamic environments in real time. The paper presents the implemented algorithm and details the proposed architecture for executing it, exposing in particular the partitioning decisions made to meet the required performance. A prototype implementation is described and the results obtained are discussed with respect to other state-of-the-art solutions.

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Acknowledgments

This work has been sponsored by the French government research programme “Investissements d’avenir” through the IMobS3 Laboratory of Excellence (ANR-10-LABX-16-01), by the European Union through the program Regional competitiveness and employment 2007-2013 (ERDF Auvergne region), and by the Auvergne region.

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Authors and Affiliations

  1. TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy

    Luca Maggiani

  2. Institute Pascal, Universite Blaise Pascal, Clermont Ferrand, France

    Luca Maggiani, Cédric Bourrasset, Jean-Charles Quinton, François Berry & Jocelyn Sérot

  3. Laboratory Jean Kuntzmann, Grenoble Alpes University, Grenoble, France

    Jean-Charles Quinton

Authors
  1. Luca Maggiani
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  2. Cédric Bourrasset
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  3. Jean-Charles Quinton
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  4. François Berry
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  5. Jocelyn Sérot
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Correspondence to Luca Maggiani.

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Maggiani, L., Bourrasset, C., Quinton, JC. et al. Bio-inspired heterogeneous architecture for real-time pedestrian detection applications. J Real-Time Image Proc 14, 535–548 (2018). https://doi.org/10.1007/s11554-016-0581-3

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