FPGA implementation of object recognition processor for HDTV resolution video using sparse FIND feature

This paper describes FPGA implementation of object recognition processor for HDTV resolution 30 fps video using the Sparse FIND feature. Two-stage feature extraction processing by HOG and Sparse FIND, a highly parallel classification in the support vector machine (SVM), and a block-parallel processing for RAM access cycle reduction are proposed to perform a real time object recognition with enormous computational complexity. From implementation of the proposed architecture in the FPGA, it was confirmed that detection using the Sparse FIND feature was performed for HDTV images at 47.63 fps, on average, at 90 MHz. The recognition accuracy degradation from the original Sparse FIND-base object detection algorithm implemented on software was 0.5%, which shows that the FPGA system provides sufficient accuracy for practical use.