Pedestrian detection is a crucial task in several video surveillance and automotive scenarios, but only a few detection systems are designed to be realized on an embedded architecture, allowing to increase the processing speed which is one of the key requirements in real applications. In this paper, we propose a novel SoC (System on Chip) architecture for fast pedestrian detection in video. Our implementation is based on a linear SVM (Support Vector Machine) classification framework, learned on a set of overlapped image patches. Each patch is described by a covariance matrix of a set of image features. Exploiting the inner parallelism of the FPGA (Field Programmable Gate Array) boards, we dramatically accelerate the covariance matrices computation that plays a crucial role in the framework. In the experiments, we show the effectiveness and the efficiency of our pedestrian detection system, reaching a detection speed of 132 fps at VGA resolution.

FAST FPGA-BASED ARCHITECTURE FOR PEDESTRIAN DETECTION BASED ON COVARIANCE MATRICES

MARTELLI, Samuele;TOSATO, Diego;CRISTANI, Marco;MURINO, Vittorio
2011-01-01

Abstract

Pedestrian detection is a crucial task in several video surveillance and automotive scenarios, but only a few detection systems are designed to be realized on an embedded architecture, allowing to increase the processing speed which is one of the key requirements in real applications. In this paper, we propose a novel SoC (System on Chip) architecture for fast pedestrian detection in video. Our implementation is based on a linear SVM (Support Vector Machine) classification framework, learned on a set of overlapped image patches. Each patch is described by a covariance matrix of a set of image features. Exploiting the inner parallelism of the FPGA (Field Programmable Gate Array) boards, we dramatically accelerate the covariance matrices computation that plays a crucial role in the framework. In the experiments, we show the effectiveness and the efficiency of our pedestrian detection system, reaching a detection speed of 132 fps at VGA resolution.
2011
9781457713040
Classification; SVM; Riemannian Manifolds; Pedestrian detection and classification; FPGA; embedded systems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/366795
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