The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Detecting Pedestrians Using Patterns of Motion and Appearance
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Citations
Cites
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Research works printed but not linked: 10
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- Crow, F. 1984. Summed-area tables for texture mapping. In Proceedings of SIGGRAPH, Vol. 18, No. 3, pp. 207?212. · DOI · Paper page
- Cutler, R. and Davis, L. 2000. Robust real-time periodic motion detection: Analysis and applications. In IEEE Patt. Anal. Mach. Intell., Vol. 22, pp. 781?796. · DOI · Paper page
- Philomin, V. and Gavrila, D. 1999. Real-time object detection for ?smart vehicles.? In IEEE International Conference on Computer Vision, pp. 87?93.
- Liu, F. and Picard, R. 1998. Finding periodicity in space and time. In IEEE International Conference on Computer Vision, pp. 376?383. · DOI
- Papageorgiou, C., Oren, M., and Poggio, T. 1998. A general frame-work for object detection. In International Conference on Computer Vision.
- Polana, R. and Nelson, R. 1994. Detecting activities. Journal of Visual Communication and Image Representation. · doi:10.1006/jvci.1994.1016
- Schapire, R. and Singer, Y. 1999. Improving boosting algorithms using confidence-rated predictions. · DOI · Paper page
- Schneiderman, H. and Kanade, T. 2000. A statistical method for 3D object detection applied to faces and cars. In International Conference on Computer Vision. · DOI · Paper page
- Viola, P. and Jones, M. 2001. Rapid object detection using a boosted cascade of simple features. In IEEE Conference on Computer Vision and Pattern Recognition. · DOI · Paper page
Cited by
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
One-shot learning of object categories
Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.79 · Cited by 3,029
An HOG-LBP human detector with partial occlusion handling
Wang, X., Han, T. X., & Yan, S. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459207 · Cited by 1,727
1 year, 1000 km: The Oxford RobotCar dataset
Maddern, W., Pascoe, G., Linegar, C., & Newman, P. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916679498 · Cited by 1,632
Pedestrian detection: A benchmark
Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206631 · Cited by 1,313
Metadata
- Authors
- Viola, P., Jones, M. J., & Snow, D.
- Venue
- International Journal of Computer Vision
- Published
- 2005-02-01
- Volume
- 63
- Issue
- 2
- Pages
- 153-161
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W1992825118
- Cited by
- 2,079
- References
- 2
- FWCI
- 161.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
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- CoChat. (2026). Citation check: Detecting Pedestrians Using Patterns of Motion and Appearance. https://app.cochat.ai/papers/10.1007/s11263-005-6644-8
- The paper
- Viola, P., Jones, M. J., & Snow, D. (2005). Detecting Pedestrians Using Patterns of Motion and Appearance. International Journal of Computer Vision. https://doi.org/10.1007/s11263-005-6644-8
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.
