The DOI matches its Crossref record.
Optical Camera Based Pedestrian Detection in Rainy Or Snowy Weather
Overview
Integrity signals
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Cascaded classification incorporating local and global features increases detection rates for optical camera-based pedestrian recognition under adverse weather.
“The study in [151] tackles the issue of optical camera-based pedestrian recognition under adverse weather conditions by employing a cascaded classification technique that incorporates both local and global features, resulting in increased detection rates.”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [151]
The cascaded classification strategy is effective in challenging weather conditions such as snow or rain.
“The effectiveness of this strategy is demonstrated in challenging weather conditions such as snow or rain.”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [151]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 9
- Viola, P., Jones, M., Snow, D.: Detecting Pedestrians Using Patterns of Motion and Appearance. International Journal of Computer Vision 63(2), 153–161 (2005) · DOI
- Xu, Y.W., Cao, X.B., Qiao, H.: A low cost pedestrian detection system. IEEE WCICA (June 2006) (accepted)
- Amnon, S., Yoram, G., Gaby, H.: Pedestrian Detection for Driving Assis-tance Systems - Single-frame Classification and System Level Performance. In: IEEE Intelligent Vehicles Symposium, pp.1–6 (2004)
- Bertozzi, M., Broggi, A., Lasagni, A., Del Rose, M.: Infrared stereo vision-based pedestrian detection. In: IEEE Intelligent Vehicles Symposium, pp. 24–29 (2005) · DOI
- Xu, F., Liu, X., Fujimura, K.: Pedestrian Detection and Tracking with Night Vision. IEEE Transaction on Intelligent Transportation Systems 6(1), 63–71 (2005) · DOI
- Gavrila, D.M., Giebel, J., Munder, S.: Vision-based pedestrian detection: the PROTECTOR system. In: IEEE Intelligent Vehicles Symposium, pp. 13–18 (2004) · DOI
- Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995) · DOI
- Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods—Support Vector Learning, MIT Press, Cambridge (1998)
- Zhang, X.G.: Introduction to statistical learning theory and support vector machines. Acta Automatica Sinica 26(1), 32–42 (2000)
Cited by
Sorted by citations
Tahir, N. U. A., Zhang, Z., Asim, M., Chen, J., & ELAffendi, M. (2024) · Algorithms · doi:10.3390/a17030103 · Cited by 145
Metadata
- Authors
- Xu, Y. W., Cao, X. B., & Qiao, H.
- Affiliations
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2006
- Pages
- 1182-1191
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1500163108
- Cited by
- 4
- References
- 0
- Citations by year
- 2024: 1 · 2021: 1
- FWCI
- 0.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- pedestrian detection, rainy weather, optical camera, cascaded classification, local appearance features, false positive reduction, low-light conditions, partial occlusion
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.
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