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Sensors 2018, 18(4), 1174;

Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System

Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan
Author to whom correspondence should be addressed.
Received: 7 February 2018 / Revised: 9 April 2018 / Accepted: 9 April 2018 / Published: 12 April 2018
(This article belongs to the Section Intelligent Sensors)
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In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate. View Full-Text
Keywords: HOG; SVM; FPGA; hardware acceleration; pedestrian detection HOG; SVM; FPGA; hardware acceleration; pedestrian detection

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Luo, J.H.; Lin, C.H. Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System. Sensors 2018, 18, 1174.

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