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
컴퓨터 비전 분야의 연구 핫스팟이자 어려움으로 보행자 감지는 지능형 운전 및 교통 모니터링에 널리 사용되었습니다. 현재 널리 사용되는 탐지 방법은 RPN(Region Proposal Network)을 사용하여 후보 영역을 생성한 후 영역을 분류합니다. 그러나 RPN은 잘못된 후보 영역을 많이 생성하여 오탐에 대한 지역 제안이 증가하게 만듭니다. 이 문자는 향상된 잔여 주의 네트워크를 사용하여 이미지의 시각적 주의 지도를 캡처한 다음 정규화하여 주의 점수 지도를 얻습니다. 주의 점수 맵은 RPN 네트워크가 잠재적인 대상 객체를 포함하는 보다 정확한 후보 영역을 생성하도록 안내하는 데 사용됩니다. RPN에 의해 생성된 지역 제안, 신뢰 점수 및 기능은 최종 결과를 얻기 위해 계단식 부스트 포레스트 분류기를 훈련하는 데 사용됩니다. 실험 결과는 우리가 제안한 접근 방식이 Caltech 및 ETH 데이터 세트에서 매우 경쟁력 있는 결과를 달성한다는 것을 보여줍니다.
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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부
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
부
@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},}
부
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 -