The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis
Abstract
This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.
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Cites
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Self-citation
Cited by
Autonomous vehicle perception: The technology of today and tomorrow
Van Brummelen, J., O’Brien, M., Gruyer, D., & Najjaran, H. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.02.012 · Cited by 869
Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657
Vehicle Detection Techniques for Collision Avoidance Systems: A Review
Mukhtar, A., Xia, L., & Tang, T. B. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2409109 · Cited by 427
SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection
Hu, X., Xu, X., Xiao, Y., Chen, H., He, S., Qin, J., & Heng, P.-A. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2838132 · Cited by 341
Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
De Ponte Müller, F. (2017) · Sensors · doi:10.3390/s17020271 · Cited by 258
Metadata
- Authors
- Sivaraman, S., & Trivedi, M. M.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2013-12
- Volume
- 14
- Issue
- 4
- Pages
- 1773-1795
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2165065922
- Cited by
- 908
- References
- 10
- FWCI
- 110.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sivaraman, S., & Trivedi, M. M. (2013). Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2013.2266661
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
