In this study, we propose a hardware-oriented Gaussian mixture model – multiresolution co-occurrence histograms of oriented gradients (GMM–MRCoHOG) algorithm for efficient human detection by a field-programmable gate array (FPGA). GMM–MRCoHOG is a HOG-based human detection method in which the computation of angles is quantized to 36 directions and 2D Gaussian distribution computation causes a decrease in processing speed and an increase in hardware resource usage. We propose a hardware-oriented algorithm to solve these problems. First, we propose a rough angle computation method of comparison with a tangent table. Second, we propose a bit-shifting-based Gaussian distribution computation method. Experimental results show that the proposed hardware-oriented algorithm does not significantly reduce the detection accuracy of GMM–MRCoHOG. High-level synthesis results of the FPGA implementation show that fast, low-resource processing is possible.
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