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Journal Articles Journal of Real-Time Image Processing Year : 2018

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

Cédric Bourrasset
François Berry
Jocelyn Sérot

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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Dates and versions

hal-01298676 , version 1 (07-04-2016)

Identifiers

Cite

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, 2018, 14 (3), pp.535-548. ⟨10.1007/s11554-016-0581-3⟩. ⟨hal-01298676⟩
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