FPGA-Based Pedestrian Detection Using Array of Covariance Features

In this paper we propose a pedestrian detection algorithm and its implementation on a Xilinx Virtex-4 FPGA. The algorithm is a sliding window-based classifier, that exploits a recently designed descriptor, the covariance of features, for characterizing pedestrians in a robust way. In the paper we show how such descriptor, originally suited for maximizing accuracy performances without caring about timings, can be quickly computed in an elegant, parallel way on the FPGA board. A grid of overlapped covariances extracts information from the sliding window, and feeds a linear Support Vector Machine that performs the detection. Experiments are performed on the INRIA pedestrian benchmark; the performances of the FPGA-based detector are discussed in terms of required computational effort and accuracy, showing state-of-the-art detection performances under excellent timings and economic memory usage.