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

Luca Maggiani (1, 2) , Cédric Bourrasset (2) , Jean-Charles Quinton (2, 3) , François Berry (2) , Jocelyn Sérot (2)

1 SSSUP - Scuola Universitaria Superiore Sant'Anna [Pisa]
2 IP - Institut Pascal
3 SVH - Statistique pour le Vivant et l’Homme

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.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]

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⟩