Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection
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There are fewer works on multi-modal semantic segmentation compared to multi-modal object detection.
“Compared to the object detection problem summarized in Sec. IV-A, there are fewer works on multi-modal semantic segmentation: [92], [119], [124] employ RGB and thermal images, [61] fuses RGB images and depth images from a stereo camera, [125]-[127] combine RGB, thermal, and depth images for semantic segmentation in diverse environments such as forests, [123] fuses RGB images and LiDAR point clouds”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [119]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · arXiv · doi:10.48550/arxiv.1604.01685 · Cited by 1,174 · see citing works
Is Faster R-CNN Doing Well for Pedestrian Detection?
Zhang, L., Lin, L., Liang, X., & He, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_28 · Cited by 852 · see citing works
Filtered channel features for pedestrian detection
Zhang, S., Benenson, R., & Schiele, B. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298784 · Cited by 395 · see citing works
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Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works
Deep multimodal fusion for semantic image segmentation: A survey
Zhang, Y., Sidibé, D., Morel, O., & Mériaudeau, F. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104042 · Cited by 261 · see citing works
Zhang, Q., Zhao, S., Luo, Y., Zhang, D., Huang, N., & Han, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00266 · Cited by 225 · see citing works
- Authors
- Guan, D., Cao, Y., Yang, J., Cao, Y., & Yang, M. Y.
- Affiliations
- State Key Laboratory Fluid Power and Mechatronic Systems (CN)
- University of Twente (NL)
- Corresponding author
- Cao, Yanpeng
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 50
- Pages
- 148-157
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2964027659
- Cited by
- 342
- References
- 7
- Citations by year
- 2026: 37 · 2025: 57 · 2024: 61 · 2023: 50 · 2022: 50 · 2021: 45 · 2020: 27 · 2019: 12 · 2018: 3
- FWCI
- 10.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Information Fusion (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- University of Twente Research Information (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (51605428, 51575486, U1664264)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- lighting conditions, two-stream convolutional neural network, semantic segmentation, multi-task learning, human detection
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