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
Sebagai tempat tumpuan penyelidikan dan kesukaran dalam bidang penglihatan komputer, pengesanan pejalan kaki telah digunakan secara meluas dalam pemanduan pintar dan pemantauan lalu lintas. Kaedah pengesanan yang popular pada masa ini menggunakan rangkaian cadangan wilayah (RPN) untuk menjana wilayah calon, dan kemudian mengelaskan wilayah tersebut. Tetapi RPN menghasilkan banyak kawasan calon yang salah, menyebabkan cadangan wilayah untuk positif palsu meningkat. Surat ini menggunakan rangkaian perhatian sisa yang dipertingkatkan untuk menangkap peta perhatian visual imej, kemudian dinormalkan untuk mendapatkan peta skor perhatian. Peta skor perhatian digunakan untuk membimbing rangkaian RPN untuk menjana kawasan calon yang lebih tepat yang mengandungi objek sasaran yang berpotensi. Cadangan wilayah, skor keyakinan dan ciri yang dijana oleh RPN digunakan untuk melatih pengelas hutan dirangsang bertingkat untuk mendapatkan hasil akhir. Keputusan percubaan menunjukkan bahawa pendekatan yang dicadangkan kami mencapai hasil yang sangat kompetitif pada set data Caltech dan ETH.
Rui SUN
Hefei University of Technology
Huihui WANG
Hefei University of Technology
Jun ZHANG
Hefei University of Technology
Xudong ZHANG
Hefei University of Technology
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Salinan
Rui SUN, Huihui WANG, Jun ZHANG, Xudong ZHANG, "Attention-Guided Region Proposal Network for Pedestrian Detection" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 10, pp. 2072-2076, October 2019, doi: 10.1587/transinf.2019EDL8027.
Abstract: As a research hotspot and difficulty in the field of computer vision, pedestrian detection has been widely used in intelligent driving and traffic monitoring. The popular detection method at present uses region proposal network (RPN) to generate candidate regions, and then classifies the regions. But the RPN produces many erroneous candidate areas, causing region proposals for false positives to increase. This letter uses improved residual attention network to capture the visual attention map of images, then normalized to get the attention score map. The attention score map is used to guide the RPN network to generate more precise candidate regions containing potential target objects. The region proposals, confidence scores, and features generated by the RPN are used to train a cascaded boosted forest classifier to obtain the final results. The experimental results show that our proposed approach achieves highly competitive results on the Caltech and ETH datasets.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8027/_p
Salinan
@ARTICLE{e102-d_10_2072,
author={Rui SUN, Huihui WANG, Jun ZHANG, Xudong ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Attention-Guided Region Proposal Network for Pedestrian Detection},
year={2019},
volume={E102-D},
number={10},
pages={2072-2076},
abstract={As a research hotspot and difficulty in the field of computer vision, pedestrian detection has been widely used in intelligent driving and traffic monitoring. The popular detection method at present uses region proposal network (RPN) to generate candidate regions, and then classifies the regions. But the RPN produces many erroneous candidate areas, causing region proposals for false positives to increase. This letter uses improved residual attention network to capture the visual attention map of images, then normalized to get the attention score map. The attention score map is used to guide the RPN network to generate more precise candidate regions containing potential target objects. The region proposals, confidence scores, and features generated by the RPN are used to train a cascaded boosted forest classifier to obtain the final results. The experimental results show that our proposed approach achieves highly competitive results on the Caltech and ETH datasets.},
keywords={},
doi={10.1587/transinf.2019EDL8027},
ISSN={1745-1361},
month={October},}
Salinan
TY - JOUR
TI - Attention-Guided Region Proposal Network for Pedestrian Detection
T2 - IEICE TRANSACTIONS on Information
SP - 2072
EP - 2076
AU - Rui SUN
AU - Huihui WANG
AU - Jun ZHANG
AU - Xudong ZHANG
PY - 2019
DO - 10.1587/transinf.2019EDL8027
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2019
AB - As a research hotspot and difficulty in the field of computer vision, pedestrian detection has been widely used in intelligent driving and traffic monitoring. The popular detection method at present uses region proposal network (RPN) to generate candidate regions, and then classifies the regions. But the RPN produces many erroneous candidate areas, causing region proposals for false positives to increase. This letter uses improved residual attention network to capture the visual attention map of images, then normalized to get the attention score map. The attention score map is used to guide the RPN network to generate more precise candidate regions containing potential target objects. The region proposals, confidence scores, and features generated by the RPN are used to train a cascaded boosted forest classifier to obtain the final results. The experimental results show that our proposed approach achieves highly competitive results on the Caltech and ETH datasets.
ER -