The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Trainable classifier-fusion schemes: An application to pedestrian detection
Abstract
This work proposes a novel classifier-fusion scheme using learning algorithms, i.e. syntactic models, instead of the usual Bayesian or heuristic rules. Moreover, this paper complements the previous comparative studies on DaimlerChrysler Automotive Dataset, offering a set of complementary experiments using feature extractor and classifier combinations. The experimental results provide evidence of the effectiveness of our methods regarding false positive rate, AUC, and accuracy, which reached 96.67%.
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
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
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
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Cited by
Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition
Kang, L.-W., Lin, C.-W., & Fu, Y.-H. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2179057 · Cited by 905
A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
Rosique, F., Navarro, P. J., Fernández, C., & Padilla, A. (2019) · Sensors · doi:10.3390/s19030648 · Cited by 469
Metadata
- Authors
- Junior, O. L., Delgado, D., Goncalves, V., & Nunes, U.
- Venue
- 2009 12th International IEEE Conference on Intelligent Transportation Systems
- Publisher
- IEEE
- Published
- 2009-10
- Pages
- 1-6
- OpenAlex
- W2132254548
- Cited by
- 229
- References
- 3
- FWCI
- 3.82
- Type
- conference paper
- 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: Trainable classifier-fusion schemes: An application to pedestrian detection. https://app.cochat.ai/papers/10.1109/itsc.2009.5309700
- The paper
- Junior, O. L., Delgado, D., Goncalves, V., & Nunes, U. (2009). Trainable classifier-fusion schemes: An application to pedestrian detection. 2009 12th International IEEE Conference on Intelligent Transportation Systems. https://doi.org/10.1109/itsc.2009.5309700
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.
