The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Detection of Traffic and Road Condition Based on SVM and PHOW
Open accessAbstract
To avoid the defects of physical detection methods, such as road spoiling, complex algorithms and equipment maintenance, SVM (Support Vector Machine) and PHOW (Pyramid Histogram of Words) are explored to detect the traffic and road condition. Experimental results show that the average accuracies go beyond 82.5% in the case of different weather. The proposed method can effectively classify 4 types of traffic and road condition images.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“The experimental findings show an accuracy of more than 82.5% on average under different weather circumstances.”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [101]
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Flagged first, then by citations
Research works printed but not linked: 8
- Fan Xiaoke. Research on the Problem of Urban Traffic Congestion[J]. China Public Security: Academy Edition. 2007(1): pp.48-51.
- Lazebnik S, Schmid C, and Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories[C]. Proceedings of the IEEE Computer Society Conference of Computer Vision and Pattern Recognition (CVPR'06), New York, USA, June 17-22, 2006, Vol 2: 2169-2178. · DOI · Paper page
- Bosch, A., X. Muñoz, and R. Martí, Which is the best way to organize/classify images by content[J] Image and vision computing, 2007. 25(6): pp.778-791. · DOI
- JiaShijie, KongXiangwei, FuHaiyan, etal. Auto Classification of Product Images Based on Complementary Features and Class Descriptor[J]. Journal of Electronics and Information Technology. 2010. 32(10): pp.2294-2300. · DOI
- Wu, X., V. Kumar. The Top Ten Algorithms in Data Mining[M]. Boca Raton, FL : Chapman and Hall/CRC. Vol. 9. (2009). · DOI
- Yong. H, Chun-ying. Z, Zhong-hu.S. Multi-class Support Vector Machines Algorithm Summarization [J]. Computing Technology and Automation. 2005, 24(4): pp.1003-6199.
- Bosch A, Zisserman A, and Munoz X. Representing shape with a spatial pyramid kernel[C]. Proceedings of the 6th ACM international conference on Image and video retrieval, Amsterdam, Dutch, 2007: 401-408. · DOI
- He, K., J. Sun, and X. Tang, Single image haze removal using dark channel prior[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2011. 33(12): pp.2341-2353. · DOI · Paper page
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
- Jia, S. J., Zhai, Y. T., & Jia, X. W.
- Affiliations
- Dalian Jiaotong University (CN)
- Shandong College of Information Technology (CN)
- Corresponding author
- Jia, Shi Jie
- ORCID
- Jia, Xiao Wei
- Venue
- Applied Mechanics and Materials
- Publisher
- Trans Tech Publications, Ltd.
- Published
- 2014-02-06
- Volume
- 513-517
- Pages
- 3651-3654
- ISSN
- 1660-9336 (ISSN-L)
- 1662-7482
- OpenAlex
- W2094285870
- Cited by
- 1
- References
- 1
- Citations by year
- 2024: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Applied Mechanics and Materials (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- road condition detection, support vector machine, image classification
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
