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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Journal of Real-Time Image Processing, Springer Verlag, 2018, 14 (3), pp.535-548. 〈10.1007/s11554-016-0581-3〉
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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, 2018, 14 (3), pp.535-548. 〈10.1007/s11554-016-0581-3〉. 〈hal-01298676〉

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