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Weakly Aligned Feature Fusion for Multimodal Object Detection

文献类型：期刊论文

作者Zhang, Lu5,6; Liu, Zhiyong2,5,6; Zhu, Xiangyu3,4,6; Song, Zhan1; Yang, Xu2,5,6; Lei, Zhen3,4,6,7; Qiao, Hong5,6
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-08-25
页码15
关键词Object detection Feature extraction Detectors Robustness Cameras Automation Training Deep learning feature fusion multimodal object detection pedestrian detection
ISSN号2162-237X
DOI10.1109/TNNLS.2021.3105143
通讯作者Liu, Zhiyong(zhiyong.liu@ia.ac.cn)
英文摘要To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, and depth. However, multimodal data often suffer from the position shift problem, i.e., the image pair is not strictly aligned, making one object has different positions in different modalities. For the deep learning method, this problem makes it difficult to fuse multimodal features and puzzles the convolutional neural network (CNN) training. In this article, we propose a general multimodal detector named aligned region CNN (AR-CNN) to tackle the position shift problem. First, a region feature (RF) alignment module with adjacent similarity constraint is designed to consistently predict the position shift between two modalities and adaptively align the cross-modal RFs. Second, we propose a novel region of interest (RoI) jitter strategy to improve the robustness to unexpected shift patterns. Third, we present a new multimodal feature fusion method that selects the more reliable feature and suppresses the less useful one via feature reweighting. In addition, by locating bounding boxes in both modalities and building their relationships, we provide novel multimodal labeling named KAIST-Paired. Extensive experiments on 2-D and 3-D object detection, RGB-T, and RGB-D datasets demonstrate the effectiveness and robustness of our method.
WOS关键词MULTISPECTRAL PEDESTRIAN DETECTION ; DEEP NEURAL-NETWORKS ; ACCURATE
资助项目National Key Research and Development Plan of China[2020AAA0108902] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100] ; Beijing Science and Technology Plan Project[Z201100008320029] ; NSFC[61627808] ; NSFC[61876178] ; NSFC[61806196] ; Dongguan Core Technology Research Frontier Project, China[2019622101001]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000732172000001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Plan of China ; Strategic Priority Research Program of Chinese Academy of Science ; Beijing Science and Technology Plan Project ; NSFC ; Dongguan Core Technology Research Frontier Project, China
源URL[http://ir.ia.ac.cn/handle/173211/46872]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者Liu, Zhiyong
作者单位1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Guangdong, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
3.Chinese Acad Sci, Ctr Biometr & Secur Res, Inst Automat, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100086, Peoples R China
7.Chinese Acad Sci, Ctr Artificial Intelligence & Robot, Hong Kong Inst Sci & Innovat, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Lu,Liu, Zhiyong,Zhu, Xiangyu,et al. Weakly Aligned Feature Fusion for Multimodal Object Detection[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:15.
APA Zhang, Lu.,Liu, Zhiyong.,Zhu, Xiangyu.,Song, Zhan.,Yang, Xu.,...&Qiao, Hong.(2021).Weakly Aligned Feature Fusion for Multimodal Object Detection.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA Zhang, Lu,et al."Weakly Aligned Feature Fusion for Multimodal Object Detection".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):15.

入库方式： OAI收割

来源：自动化研究所

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