The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Predicting the Driver's Focus of Attention: The DR(eye)VE Project
Open accessAbstract
In this work we aim to predict the driver's focus of attention. The goal is to estimate what a person would pay attention to while driving, and which part of the scene around the vehicle is more critical for the task. To this end we propose a new computer vision model based on a multi-branch deep architecture that integrates three sources of information: raw video, motion and scene semantics. We also introduce DR(eye)VE, the largest dataset of driving scenes for which eye-tracking annotations are available. This dataset features more than 500,000 registered frames, matching ego-centric views (from glasses worn by drivers) and car-centric views (from roof-mounted camera), further enriched by other sensors measurements. Results highlight that several attention patterns are shared across drivers and can be reproduced to some extent. The indication of which elements in the scene are likely to capture the driver's attention may benefit several applications in the context of human-vehicle interaction and driver attention analysis.
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Cites
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Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
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Latent Space Autoregression for Novelty Detection
Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00057 · Cited by 508
Self-citationExplainable Object-Induced Action Decision for Autonomous Vehicles
Xu, Y., Yang, X., Gong, L., Lin, H.-C., Wu, T.-Y., Li, Y., & Vasconcelos, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00954 · Cited by 129
Human Gaze Assisted Artificial Intelligence: A Review
Zhang, R., Saran, A., Liu, B., Zhu, Y., Guo, S., Niekum, S., Ballard, D., & Hayhoe, M. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/689 · Cited by 48
A Review of Detection and Removal of Raindrops in Automotive Vision Systems
Hamzeh, Y., & Rawashdeh, S. A. (2021) · Journal of Imaging · doi:10.3390/jimaging7030052 · Cited by 22
“augmented videos from DR(eye)VE dataset [66] with synthetically generated raindrops.”
Method
Metadata
- Authors
- Palazzi, A., Abati, D., Calderara, S., Solera, F., & Cucchiara, R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2019-07-01
- Volume
- 41
- Issue
- 7
- Pages
- 1720-1733
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2612448678
- Cited by
- 313
- References
- 70
- FWCI
- 13.58
- Type
- journal article
- Language
- en
- Open access
- green
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- Palazzi, A., Abati, D., Calderara, S., Solera, F., & Cucchiara, R. (2019). Predicting the Driver's Focus of Attention: The DR(eye)VE Project. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2018.2845370
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