The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
Abstract
This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a generic topographical scene description from underlying motion structure as observed over time. The scene topology is automatically learned and is distinguished by points of interest and motion characterized by activity paths. The methods we review are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction.
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
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cited by
Kitani, K. M., Ziebart, B. D., Bagnell, J. A., & Hebert, M. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33765-9_15 · Cited by 701
Pallotta, G., Vespe, M., & Bryan, K. (2013) · Entropy · doi:10.3390/e15062218 · Cited by 695
Human motion trajectory prediction: a survey
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920917446 · Cited by 658
Transformer Networks for Trajectory Forecasting
Giuliari, F., Hasan, I., Cristani, M., & Galasso, F. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9412190 · Cited by 572
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-citation
Metadata
- Authors
- Morris, B. T., & Trivedi, M. M.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2008-08
- Volume
- 18
- Issue
- 8
- Pages
- 1114-1127
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2133235827
- Cited by
- 474
- References
- 1
- FWCI
- 42.00
- 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: A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance. https://app.cochat.ai/papers/10.1109/tcsvt.2008.927109
- The paper
- Morris, B. T., & Trivedi, M. M. (2008). A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/tcsvt.2008.927109
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.
