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

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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Contributeur : Luca Maggiani <>
Soumis le : jeudi 7 avril 2016 - 10:37:12
Dernière modification le : lundi 9 avril 2018 - 12:22:50
Document(s) archivé(s) le : lundi 14 novembre 2016 - 17:37:29


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Luca Maggiani, Cédric Bourrasset, Jean-Charles Quinton, François Berry, Jocelyn Sérot. Bio-inspired heterogeneous architecture for real-time pedestrian detection applications. Journal of Real-Time Image Processing, Springer Verlag, 2016, 〈10.1007/s11554-016-0581-3〉. 〈hal-01298676〉



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