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.