NDLI: Hardware architecture and optimization of sliding window based pedestrian detection on FPGA for high resolution images by varying local features

Content Provider	IEEE Xplore Digital Library
Author	Khan, A. Khan, M.U.K. Bilal, M. Chong-Min Kyung
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol. (KAIST), Daejeon, South Korea (Khan, A.; Khan, M.U.K.; Bilal, M.; Chong-Min Kyung)
Abstract	Pedestrian detection has lately attracted considerable interest from researchers due to many practical applications. However, the low accuracy and high complexity of pedestrian detection has still not enabled its use in successful commercial applications. In this paper, we present insights into the complexity-accuracy relationship of pedestrian detection. We consider the Histogram of Oriented Gradients (HOG) scheme with linear Support Vector Machine (LinSVM) as a benchmark. We describe parallel implementations of various blocks of the pedestrian detection system which are designed for full-HD (1920×1080) resolution. Features are improved by optimal selection of cell size and histogram bins which have been shown to significantly affect the accuracy and complexity of pedestrian detection. It is seen that with a careful choice of these parameters a frame rate of 39.2 fps is achieved with a negligible loss in accuracy which is 16.3x and 3.8x higher than state of the art GPU and FPGA implementations respectively. Moreover 97.14% and 10.2% reduction in energy consumption is observed to process one frame. Finally, features are further enhanced by removing petty gradients in histograms which result in loss of accuracy. This increases the frame rate to 42.7 fps (18x and 4.1x higher) and lowers the energy consumption by 97.34% and 16.4% while improving the accuracy by 2% as compared to state of the art GPU and FPGA implementations respectively.
Starting Page	142
Ending Page	148
File Size	786485
Page Count	7
File Format	PDF
ISSN	23248440
e-ISBN	9781467391405
DOI	10.1109/VLSI-SoC.2015.7314406
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-05
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Real-Time Histograms Object Detection Accuracy Microprocessors FPGA Low Power Computer architecture Feature extraction Hardware Field programmable gate arrays
Content Type	Text
Resource Type	Article

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