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

• Published in: Journal of real-time image processing Vol. 14; no. 3; pp. 535 - 548
• Main Authors: Maggiani, Luca, Bourrasset, Cédric, Quinton, Jean-Charles, Berry, François, Sérot, Jocelyn
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2018; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Artificial Intelligence; Classification; Co-design; Computer Graphics; Computer Science; Computer Vision and Pattern Recognition; Human error; Image Processing and Computer Vision; Machine Learning; Multimedia Information Systems; Pattern Recognition; Real time; Signal,Image and Speech Processing; Special Issue Paper; Support vector machines; Wavelet transforms; Heterogeneous; Co-design; FPGA; DNF; Real-time
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-016-0581-3

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.