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
보행자 감지는 컴퓨터 비전에서 중요한 작업입니다. 최근에는 지능형 감시 시스템, 자율주행 시스템 등의 애플리케이션에 널리 사용되고 있다. 지난 1년 동안 철저하게 연구되었지만 교합 처리 문제는 여전히 해결되지 않은 상태로 남아 있습니다. 유력한 아이디어 중 하나는 먼저 인체 부위를 감지한 후 부품 정보를 활용하여 보행자의 존재를 추정하는 것입니다. 이 아이디어를 기반으로 많은 부품 기반 보행자 감지 접근 방식이 제안되었습니다. 그러나 대부분의 이러한 접근 방식에서는 품질이 낮은 부품 마이닝과 서투른 부품 감지기의 조합이 감지 성능을 제한하는 병목 현상이 됩니다. 병목 현상을 제거하기 위해 DP-CNN(Discriminative Part CNN)을 제안합니다. 우리의 접근 방식에는 두 가지 주요 기여가 있습니다. (2) 컨볼루셔널 레이어 기능과 신체 부위 하위 클래스를 기반으로 하는 고품질 신체 부위 마이닝 방법을 제안합니다. 채굴된 부품 클러스터는 식별적일 뿐만 아니라 대표적이며 강력한 보행자 감지기를 구축하는 데 도움이 될 수 있습니다. (XNUMX) 여러 부품 검출기를 결합하는 새로운 방법을 제안합니다. 부품 탐지기를 CNN의 중간 계층으로 변환하고 해당 CNN을 미세 조정하여 전체 탐지 파이프라인을 최적화합니다. 실험에서는 최적화의 놀라운 효율성과 폐색 처리의 견고성을 보여줍니다.
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부
Yu WANG, Cong CAO, Jien KATO, "Discriminative Part CNN for Pedestrian Detection" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 3, pp. 700-712, March 2022, doi: 10.1587/transinf.2021EDP7057.
Abstract: Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in applications such as intelligent surveillance systems and automated driving systems. Although it has been exhaustively studied in the last decade, the occlusion handling issue still remains unsolved. One convincing idea is to first detect human body parts, and then utilize the parts information to estimate the pedestrians' existence. Many parts-based pedestrian detection approaches have been proposed based on this idea. However, in most of these approaches, the low-quality parts mining and the clumsy part detector combination is a bottleneck that limits the detection performance. To eliminate the bottleneck, we propose Discriminative Part CNN (DP-CNN). Our approach has two main contributions: (1) We propose a high-quality body parts mining method based on both convolutional layer features and body part subclasses. The mined part clusters are not only discriminative but also representative, and can help to construct powerful pedestrian detectors. (2) We propose a novel method to combine multiple part detectors. We convert the part detectors to a middle layer of a CNN and optimize the whole detection pipeline by fine-tuning that CNN. In experiments, it shows astonishing effectiveness of optimization and robustness of occlusion handling.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7057/_p
부
@ARTICLE{e105-d_3_700,
author={Yu WANG, Cong CAO, Jien KATO, },
journal={IEICE TRANSACTIONS on Information},
title={Discriminative Part CNN for Pedestrian Detection},
year={2022},
volume={E105-D},
number={3},
pages={700-712},
abstract={Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in applications such as intelligent surveillance systems and automated driving systems. Although it has been exhaustively studied in the last decade, the occlusion handling issue still remains unsolved. One convincing idea is to first detect human body parts, and then utilize the parts information to estimate the pedestrians' existence. Many parts-based pedestrian detection approaches have been proposed based on this idea. However, in most of these approaches, the low-quality parts mining and the clumsy part detector combination is a bottleneck that limits the detection performance. To eliminate the bottleneck, we propose Discriminative Part CNN (DP-CNN). Our approach has two main contributions: (1) We propose a high-quality body parts mining method based on both convolutional layer features and body part subclasses. The mined part clusters are not only discriminative but also representative, and can help to construct powerful pedestrian detectors. (2) We propose a novel method to combine multiple part detectors. We convert the part detectors to a middle layer of a CNN and optimize the whole detection pipeline by fine-tuning that CNN. In experiments, it shows astonishing effectiveness of optimization and robustness of occlusion handling.},
keywords={},
doi={10.1587/transinf.2021EDP7057},
ISSN={1745-1361},
month={March},}
부
TY - JOUR
TI - Discriminative Part CNN for Pedestrian Detection
T2 - IEICE TRANSACTIONS on Information
SP - 700
EP - 712
AU - Yu WANG
AU - Cong CAO
AU - Jien KATO
PY - 2022
DO - 10.1587/transinf.2021EDP7057
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2022
AB - Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in applications such as intelligent surveillance systems and automated driving systems. Although it has been exhaustively studied in the last decade, the occlusion handling issue still remains unsolved. One convincing idea is to first detect human body parts, and then utilize the parts information to estimate the pedestrians' existence. Many parts-based pedestrian detection approaches have been proposed based on this idea. However, in most of these approaches, the low-quality parts mining and the clumsy part detector combination is a bottleneck that limits the detection performance. To eliminate the bottleneck, we propose Discriminative Part CNN (DP-CNN). Our approach has two main contributions: (1) We propose a high-quality body parts mining method based on both convolutional layer features and body part subclasses. The mined part clusters are not only discriminative but also representative, and can help to construct powerful pedestrian detectors. (2) We propose a novel method to combine multiple part detectors. We convert the part detectors to a middle layer of a CNN and optimize the whole detection pipeline by fine-tuning that CNN. In experiments, it shows astonishing effectiveness of optimization and robustness of occlusion handling.
ER -