FPGA Implementation of Human Detection by HOG Features with AdaBoost

Abstract

SUMMARYWe implement external memory-free deep pipelined FPGA implementation including HOG feature extraction and AdaBoost classification. To construct our design by compact FPGA, we introduce some simplifications of the algorithm and aggressive use of stream oriented architectures. We present comparison results between our simplified fixed-point scheme and an original floating-point scheme in terms of quality of results, and the results suggest the negative impact of the simplified scheme for hardware implementation is limited. We empirically show that, our system is able to detect human from 640 × 480 VGA images at up to 112 FPS on a Xilinx Virtex-5 XC5VLX50 FPGA.

Field Programmable Gate Array Applications—A Scientometric Review

“…This machine learning technique is the newest that has been implemented with FPGAs in our list (starting in 2005). It has been widely applied for image processing in face detection [524][525][526][527][528] and also for human detection [501,529].…”

The Study on the Model of Effective Face Retrieval in Pedestrian Detection

“…The feature is composed of statistical histograms of gradient information in the local region of the image [2,3,4]. To accelerate calculation speed, blocked histogram is calculated with integral images.…”