FPGA-GPU Architecture for Kernel SVM Pedestrian Detection

We present a real-time multi-sensor architecture for
video-based pedestrian detection used within a road side
unit for intersection assistance. The entire system is implemented
on available PC hardware, combining a frame
grabber board with embedded FPGA and a graphics card
into a powerful processing network. Giving classification
performance top priority, we use HOG descriptors with
a Gaussian kernel support vector machine. In order to
achieve real-time performance, we propose a hardware architecture
that incorporates FPGA-based feature extraction
and GPU-based classification. The FPGA-GPU pipeline is
managed by a multi-core CPU that further performs sensor
data fusion. Evaluation on the INRIA benchmark database
and an experimental study on a real-world intersection using
multi-spectral hypothesis generation confirm state-ofthe-
art classification and real-time performance.