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Recently, in-car monitoring has emerged as a promising technology for detecting early-stage abnormal status of the driver and providing timely alerts to prevent traffic accidents.
G. J. McLachlan, “Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis,” Journal of the American Statistical Association , vol. 70, no. 350, pp. 365–369, 1975
1975
Earlier work this paper cites.
D. M. Lane, M. Chantler, D. Y. Dai, and I. T. Ruiz, “Tracking and classification of multiple objects in multibeam sector scan sonar image sequences,” in Proceedings of 1998 International Symposium on Underwater Technology . IEEE, 1998, pp. 269–273
1998
Earlier work this paper cites.
A. A. Perera, C. Srinivas, A. Hoogs, G. Brooksby, and W. Hu, “Multi-object tracking through simultaneous long occlusions and split-merge conditions,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) , vol. 1. IEEE, 2006, pp. 666–673
2006
Earlier work this paper cites.
P. Angkititrakul, M. Petracca, A. Sathyanarayana, and J. H. Hansen, “Utdrive: Driver behavior and speech interactive systems for in-vehicle environments,” in 2007 IEEE Intelligent Vehicles Symposium . IEEE, 2007, pp. 566–569
2007
Earlier work this paper cites.
Y. Ma, H. Derksen, W. Hong, and J. Wright, “Segmentation of multivariate mixed data via lossy data coding and compression,” IEEE transactions on pattern analysis and machine intelligence , vol. 29, no. 9, pp. 1546–1562, 2007
2007
Earlier work this paper cites.
S. Rao, R. Tron, R. Vidal, and Y. Ma, “Motion segmentation in the presence of outlying, incomplete, or corrupted trajectories,” IEEE transactions on pattern analysis and machine intelligence , vol. 32, no. 10, pp. 1832–1845, 2009
2009
Earlier work this paper cites.
D. S. Breed, W. E. DuVall, and W. C. Johnson, “Dynamic weight sensing and classification of vehicular occupants,” Nov. 17 2009, uS Patent 7,620,521
2009
Earlier work this paper cites.
S.-H. Cho, Y.-Y. Nam, S.-J. Hong, and W.-D. Cho, “Sector based scanning and adaptive active tracking of multiple objects,” KSII Transactions on Internet and Information Systems (TIIS) , vol. 5, no. 6, pp. 1166–1191, 2011
2011
Earlier work this paper cites.
G. Liu and S. Yan, “Latent low-rank representation for subspace segmentation and feature extraction,” in 2011 international conference on computer vision . IEEE, 2011, pp. 1615–1622
2011
Earlier work this paper cites.
M.-H. Sigari, M. Fathy, and M. Soryani, “A driver face monitoring system for fatigue and distraction detection,” International journal of vehicular technology , vol. 2013, pp. 1–11, 2013
2013
Earlier work this paper cites.
D.-H. Lee et al. , “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in Workshop on challenges in representation learning, ICML , vol. 3, no. 2. Atlanta, 2013, p. 896
2013
Earlier work this paper cites.
N. Alioua, A. Amine, and M. Rziza, “Driver’s fatigue detection based on yawning extraction,” International journal of vehicular technology , vol. 2014, 2014
2014
Earlier work this paper cites.
P. Bachman, O. Alsharif, and D. Precup, “Learning with pseudo-ensembles,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
Association for safe international road travel. accessed feb. 2015. Http://www.asirt.org/
2015
Earlier work this paper cites.
S. Kaplan, M. A. Guvensan, A. G. Yavuz, and Y. Karalurt, “Driver behavior analysis for safe driving: A survey,” IEEE Transactions on Intelligent Transportation Systems , vol. 16, no. 6, pp. 3017–3032, 2015
2015
Earlier work this paper cites.
T. A. Dingus, F. Guo, S. Lee, J. F. Antin, M. Perez, M. Buchanan-King, and J. Hankey, “Driver crash risk factors and prevalence evaluation using naturalistic driving data,” Proceedings of the National Academy of Sciences , vol. 113, no. 10, pp. 2636–2641, 2016
2016
Earlier work this paper cites.
G. Zhang, K. K. Yau, X. Zhang, and Y. Li, “Traffic accidents involving fatigue driving and their extent of casualties,” Accident Analysis & Prevention , vol. 87, pp. 34–42, 2016
2016
Earlier work this paper cites.
H. Wang, M. Naghavi, C. Allen, R. M. Barber, Z. A. Bhutta, A. Carter, D. C. Casey, F. J. Charlson, A. Z. Chen, M. M. Coates et al. , “Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the global burden of disease study 2015,” The lancet , vol. 388, no. 10053, pp. 1459–1544, 2016
2016
Earlier work this paper cites.
S. Taamneh, P. Tsiamyrtzis, M. Dcosta, P. Buddharaju, A. Khatri, M. Manser, T. Ferris, R. Wunderlich, and I. Pavlidis, “A multimodal dataset for various forms of distracted driving,” Scientific data , vol. 4, no. 1, pp. 1–21, 2017
2017
Earlier work this paper cites.
S. Yao, S. Hu, Y. Zhao, A. Zhang, and T. Abdelzaher, “Deepsense: A unified deep learning framework for time-series mobile sensing data processing,” in Proceedings of the 26th international conference on world wide web , 2017, pp. 351–360
2017
Earlier work this paper cites.
C. Zhou and R. C. Paffenroth, “Anomaly detection with robust deep autoencoders,” in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , 2017, pp. 665–674
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International conference on machine learning . PMLR, 2017, pp. 1126–1135
2017
Earlier work this paper cites.
M. N. Rastgoo, B. Nakisa, A. Rakotonirainy, V. Chandran, and D. Tjondronegoro, “A critical review of proactive detection of driver stress levels based on multimodal measurements,” ACM Computing Surveys (CSUR) , vol. 51, no. 5, pp. 1–35, 2018
2018
Earlier work this paper cites.
M. Yang, X. Yang, L. Li, and L. Zhang, “In-car multiple targets vital sign monitoring using location-based vmd algorithm,” in 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) . IEEE, 2018, pp. 1–6
2018
Earlier work this paper cites.
J. J. Fuster and K. Walsh, “Somatic mutations and clonal hematopoiesis: unexpected potential new drivers of age-related cardiovascular disease,” Circulation research , vol. 122, no. 3, pp. 523–532, 2018
2018
Earlier work this paper cites.
L.-Y. Gui, Y.-X. Wang, D. Ramanan, and J. M. Moura, “Few-shot human motion prediction via meta-learning,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 432–450
2018
Earlier work this paper cites.
I. Jegham, A. Ben Khalifa, I. Alouani, and M. A. Mahjoub, “Mdad: A multimodal and multiview in-vehicle driver action dataset,” in Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part I 18 . Springer, 2019, pp. 518–529
2019
Earlier work this paper cites.
Q. Suo, W. Zhong, F. Ma, Y. Yuan, J. Gao, and A. Zhang, “Metric learning on healthcare data with incomplete modalities.” in IJCAI , vol. 3534, 2019, p. 3540
2019
Cited alongside, same era.
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel, “Mixmatch: A holistic approach to semi-supervised learning,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
P. Perera, R. Nallapati, and B. Xiang, “Ocgan: One-class novelty detection using gans with constrained latent representations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2898–2906
2019
Cited alongside, same era.
Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele, “Meta-transfer learning for few-shot learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 403–412
2019
Cited alongside, same era.
L. H. Backar, M. A. Khalifa, and M. A.-M. Salem, “In-vehicle monitoring for passengers’ safety,” in 2022 IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin) , 2022, pp. 1–6
2022
Later among the works it cites.
2022
Later among the works it cites.
Z. Juncen, J. Cao, Y. Yang, W. Ren, and H. Han, “mmdrive: Fine-grained fatigue driving detection using mmwave radar,” ACM Transactions on Internet of Things , vol. 4, no. 4, pp. 1–30, 2023
2023
Later among the works it cites.
R. Ghorbani, M. J. Reinders, and D. M. Tax, “Self-supervised ppg representation learning shows high inter-subject variability,” in Proceedings of the 2023 8th International Conference on Machine Learning Technologies , 2023, pp. 127–132
2023
Later among the works it cites.
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M. Martin, A. Roitberg, M. Haurilet, M. Horne, S. Reiß, M. Voit, and R. Stiefelhagen, “Drive&act: A multi-modal dataset for fine-grained driver behavior recognition in autonomous vehicles,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2801–2810
2019
Cited alongside, same era.
F. Hamza Cherif, L. Hamza Cherif, M. Benabdellah, and G. Nassar, “Monitoring driver health status in real time,” Review of scientific instruments , vol. 91, no. 3, 2020
2020
Cited alongside, same era.
A. Němcová, V. Svozilová, K. Bucsuházy, R. Smíšek, M. Mézl, B. Hesko, M. Belák, M. Bilík, P. Maxera, M. Seitl et al. , “Multimodal features for detection of driver stress and fatigue,” IEEE Transactions on Intelligent Transportation Systems , vol. 22, no. 6, pp. 3214–3233, 2020
2020
Cited alongside, same era.
G. Du, T. Li, C. Li, P. X. Liu, and D. Li, “Vision-based fatigue driving recognition method integrating heart rate and facial features,” IEEE transactions on intelligent transportation systems , vol. 22, no. 5, pp. 3089–3100, 2020
2020
Cited alongside, same era.
Q. Wang, L. Zhan, P. Thompson, and J. Zhou, “Multimodal learning with incomplete modalities by knowledge distillation,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 1828–1838
2020
Cited alongside, same era.
J. D. Ortega, N. Kose, P. Cañas, M.-A. Chao, A. Unnervik, M. Nieto, O. Otaegui, and L. Salgado, “Dmd: A large-scale multi-modal driver monitoring dataset for attention and alertness analysis,” in Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part IV 16 . Springer, 2020, pp. 387–405
2020
Cited alongside, same era.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research , vol. 21, no. 140, pp. 1–67, 2020
2020
Cited alongside, same era.
K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C.-L. Li, “Fixmatch: Simplifying semi-supervised learning with consistency and confidence,” Advances in neural information processing systems , vol. 33, pp. 596–608, 2020
2020
Cited alongside, same era.
D. Yang, S. Huang, Z. Xu, Z. Li, S. Wang, M. Li, Y. Wang, Y. Liu, K. Yang, Z. Chen et al. , “Aide: A vision-driven multi-view, multi-modal, multi-tasking dataset for assistive driving perception,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 20 459–20 470
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
T. Zheng, A. Li, Z. Chen, H. Wang, and J. Luo, “Autofed: Heterogeneity-aware federated multimodal learning for robust autonomous driving,” in Proceedings of the 29th Annual International Conference on Mobile Computing and Networking , 2023, pp. 1–15
2023
Later among the works it cites.
S. Lyu, Z. Lin, G. Qu, X. Chen, X. Huang, and P. Li, “Optimal resource allocation for u-shaped parallel split learning,” in Proc. Globecom Wkshps , 2023, pp. 197–202
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
Z. Lin, G. Zhu, Y. Deng, X. Chen, Y. Gao, K. Huang, and Y. Fang, “Efficient parallel split learning over resource-constrained wireless edge networks,” IEEE Trans. Mob. Comput. , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
J. Bao, H. Sun, H. Deng, Y. He, Z. Zhang, and X. Li, “Bmad: Benchmarks for medical anomaly detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 4042–4053
2024
Closest in time.
R. Ghorbani, M. J. Reinders, and D. M. Tax, “Personalized anomaly detection in ppg data using representation learning and biometric identification,” Biomedical Signal Processing and Control , vol. 94, p. 106216, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Z. Lin, G. Qu, X. Chen, and K. Huang, “Split learning in 6g edge networks,” IEEE Wirel. Commun. , 2024
2024
Closest in time.
M. Hu, J. Zhang, X. Wang, S. Liu, and Z. Lin, “Accelerating federated learning with model segmentation for edge networks,” IEEE Transactions on Green Communications and Networking , 2024
2024
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2024
Closest in time.
2024
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2024
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