The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kami mempersembahkan sistem pengesanan pejalan kaki pembelajaran mendalam yang boleh dikonfigurasikan semula untuk sistem pengawasan yang mengesan orang yang mempunyai bayang-bayang dalam pencahayaan yang berbeza dan keadaan yang sangat tertutup. Kerja ini mencadangkan CNN berasaskan wilayah, digabungkan dengan CMOS dan kamera terma untuk mendapatkan ciri manusia walaupun dalam keadaan pencahayaan yang lemah. Kelebihan utama sistem boleh dikonfigurasikan semula berkenaan dengan sistem berasaskan pemproses ialah prestasi tinggi dan selari apabila memproses sejumlah besar data seperti bingkai video. Kami membincangkan butiran pelaksanaan perkakasan dalam algoritma pengesanan pejalan kaki masa nyata yang dicadangkan pada Zynq FPGA. Keputusan simulasi menunjukkan bahawa cadangan pendekatan bersepadu seni bina R-CNN dengan kamera memberikan prestasi yang lebih baik dari segi ketepatan, ketepatan dan skor F1. Prestasi Zynq FPGA dibandingkan dengan karya lain, yang menunjukkan bahawa seni bina yang dicadangkan adalah pertukaran yang baik dari segi kualiti, ketepatan, kelajuan dan penggunaan sumber.
M.K. JEEVARAJAN
Anna University
P. NIRMAL KUMAR
Anna University
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Salinan
M.K. JEEVARAJAN, P. NIRMAL KUMAR, "Reconfigurable Pedestrian Detection System Using Deep Learning for Video Surveillance" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 9, pp. 1610-1614, September 2023, doi: 10.1587/transinf.2019EDL8132.
Abstract: We present a reconfigurable deep learning pedestrian detection system for surveillance systems that detect people with shadows in different lighting and heavily occluded conditions. This work proposes a region-based CNN, combined with CMOS and thermal cameras to obtain human features even under poor lighting conditions. The main advantage of a reconfigurable system with respect to processor-based systems is its high performance and parallelism when processing large amount of data such as video frames. We discuss the details of hardware implementation in the proposed real-time pedestrian detection algorithm on a Zynq FPGA. Simulation results show that the proposed integrated approach of R-CNN architecture with cameras provides better performance in terms of accuracy, precision, and F1-score. The performance of Zynq FPGA was compared to other works, which showed that the proposed architecture is a good trade-off in terms of quality, accuracy, speed, and resource utilization.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8132/_p
Salinan
@ARTICLE{e106-d_9_1610,
author={M.K. JEEVARAJAN, P. NIRMAL KUMAR, },
journal={IEICE TRANSACTIONS on Information},
title={Reconfigurable Pedestrian Detection System Using Deep Learning for Video Surveillance},
year={2023},
volume={E106-D},
number={9},
pages={1610-1614},
abstract={We present a reconfigurable deep learning pedestrian detection system for surveillance systems that detect people with shadows in different lighting and heavily occluded conditions. This work proposes a region-based CNN, combined with CMOS and thermal cameras to obtain human features even under poor lighting conditions. The main advantage of a reconfigurable system with respect to processor-based systems is its high performance and parallelism when processing large amount of data such as video frames. We discuss the details of hardware implementation in the proposed real-time pedestrian detection algorithm on a Zynq FPGA. Simulation results show that the proposed integrated approach of R-CNN architecture with cameras provides better performance in terms of accuracy, precision, and F1-score. The performance of Zynq FPGA was compared to other works, which showed that the proposed architecture is a good trade-off in terms of quality, accuracy, speed, and resource utilization.},
keywords={},
doi={10.1587/transinf.2019EDL8132},
ISSN={1745-1361},
month={September},}
Salinan
TY - JOUR
TI - Reconfigurable Pedestrian Detection System Using Deep Learning for Video Surveillance
T2 - IEICE TRANSACTIONS on Information
SP - 1610
EP - 1614
AU - M.K. JEEVARAJAN
AU - P. NIRMAL KUMAR
PY - 2023
DO - 10.1587/transinf.2019EDL8132
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2023
AB - We present a reconfigurable deep learning pedestrian detection system for surveillance systems that detect people with shadows in different lighting and heavily occluded conditions. This work proposes a region-based CNN, combined with CMOS and thermal cameras to obtain human features even under poor lighting conditions. The main advantage of a reconfigurable system with respect to processor-based systems is its high performance and parallelism when processing large amount of data such as video frames. We discuss the details of hardware implementation in the proposed real-time pedestrian detection algorithm on a Zynq FPGA. Simulation results show that the proposed integrated approach of R-CNN architecture with cameras provides better performance in terms of accuracy, precision, and F1-score. The performance of Zynq FPGA was compared to other works, which showed that the proposed architecture is a good trade-off in terms of quality, accuracy, speed, and resource utilization.
ER -