The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Detecting moving shadows: algorithms and evaluation
Open accessAbstract
Moving shadows need careful consideration in the development of robust dynamic scene analysis systems. Moving shadow detection is critical for accurate object detection in video streams since shadow points are often misclassified as object points, causing errors in segmentation and tracking. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches is still lacking. In this paper, we present a comprehensive survey of moving shadow detection approaches. We organize contributions reported in the literature in four classes two of them are statistical and two are deterministic. We also present a comparative empirical evaluation of representative algorithms selected from these four classes. Novel quantitative (detection and discrimination rate) and qualitative metrics (scene and object independence, flexibility to shadow situations, and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences. These video sequences and associated "ground-truth" data are made available at http://cvrr.ucsd.edu/aton/shadow to allow for others in the community to experiment with new algorithms and metrics.
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
W/sup 4/: real-time surveillance of people and their activities
Haritaoglu, I., Harwood, D., & Davis, L. S. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868683 · Cited by 2,523
Non-parametric Model for Background Subtraction
Elgammal, A., Harwood, D., & Davis, L. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45053-x_48 · Cited by 2,118
Wallflower: principles and practice of background maintenance
Toyama, K., Krumm, J., Brumitt, B., & Meyers, B. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.791228 · Cited by 1,696
A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection
Horprasert, T. (1999) · IEEE/CVF International Conference on Computer Vision (ICCV) · Cited by 944
Image Segmentation in Video Sequences: A Probabilistic Approach
Friedman, N., & Russell, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.1539 · Cited by 833
McKenna, S. J., Jabri, S., Duric, Z., Rosenfeld, A., & Wechsler, H. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.2000.0870 · Cited by 660
Model-based object tracking in monocular image sequences of road traffic scenes
Roller, D., Daniilidis, K., & Nagel, H. H. (1993) · International Journal of Computer Vision · doi:10.1007/bf01539538 · Cited by 651
Improving shadow suppression in moving object detection with HSV color information
Cucchiara, R., Crana, C., Piccardi, M., Prati, A., & Sirotti, S. (2002) · ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585) · doi:10.1109/itsc.2001.948679 · Cited by 436
Self-citationDetection of moving cast shadows for object segmentation
Stander, J., Mech, R., & Ostermann, J. (1999) · IEEE Transactions on Multimedia · doi:10.1109/6046.748172 · Cited by 301
Detecting and tracking moving objects for video surveillance
Cohen, I., & Medioni, G. (2003) · Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) · doi:10.1109/cvpr.1999.784651 · Cited by 212
Cited by
Background subtraction in real applications: Challenges, current models and future directions
Garcia-Garcia, B., Bouwmans, T., & Rosales Silva, A. J. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2019.100204 · Cited by 337
Automatic Shadow Detection and Removal from a Single Image
Khan, S. H., Bennamoun, M., Sohel, F., & Togneri, R. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2462355 · Cited by 283
Hierarchical 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
Metadata
- Authors
- Prati, A., Mikic, I., Trivedi, M. M., & Cucchiara, R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2003-07
- Volume
- 25
- Issue
- 7
- Pages
- 918-923
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2096989099
- Cited by
- 760
- References
- 17
- FWCI
- 32.78
- Type
- journal article
- Language
- en
- Open access
- green
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: Detecting moving shadows: algorithms and evaluation. https://app.cochat.ai/papers/10.1109/tpami.2003.1206520
- The paper
- Prati, A., Mikic, I., Trivedi, M. M., & Cucchiara, R. (2003). Detecting moving shadows: algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2003.1206520
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.
