The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning, Modeling, and Classification of Vehicle Track Patterns from Live Video
Abstract
This paper presents two different types of visual activity analysis modules based on vehicle tracking. The highway monitoring module accurately classifies vehicles into eight different types and collects traffic flow statistics by leveraging tracking information. These statistics are continuously accumulated to maintain daily highway models that are used to categorize traffic flow in real time. The path modeling block is a more general analysis tool that learns the normal motions encountered in a scene in an unsupervised fashion. The spatiotemporal motion characteristics of these motion paths are encoded by a hidden Markov model. With the path definitions, abnormal trajectories are detected and future intent is predicted. These modules add realtime situational awareness to highway monitoring for high-level activity and behavior analysis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Cited by
Data-Driven Intelligent Transportation Systems: A Survey
Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158001 · Cited by 1,817
Detecting anomalous events in videos by learning deep representations of appearance and motion
Xu, D., Yan, Y., Ricci, E., & Sebe, N. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.10.010 · Cited by 474
CommonRoad: Composable benchmarks for motion planning on roads
Althoff, M., Koschi, M., & Manzinger, S. (2017) · 2017 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2017.7995802 · Cited by 443
Morris, B. T., & Trivedi, M. M. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.64 · Cited by 349
Self-citationHierarchical and Networked Vehicle Surveillance in ITS: A Survey
Tian, B., Morris, B. T., Tang, M., Liu, Y., Yao, Y., Gou, C., Shen, D., & Tang, S. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2552778 · Cited by 160
Self-citation
Metadata
- Authors
- Morris, B. T., & Trivedi, M. M.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2008-09
- Volume
- 9
- Issue
- 3
- Pages
- 425-437
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2131087111
- Cited by
- 192
- References
- 1
- FWCI
- 22.91
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Learning, Modeling, and Classification of Vehicle Track Patterns from Live Video. https://app.cochat.ai/papers/10.1109/tits.2008.922970
- The paper
- Morris, B. T., & Trivedi, M. M. (2008). Learning, Modeling, and Classification of Vehicle Track Patterns from Live Video. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2008.922970
Data sources
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.
