The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
An Experimental Study on Pedestrian Classification
Abstract
Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; multiple feature-classifier combinations are examined with respect to their ROC performance and efficiency. We investigate global versus local and adaptive versus nonadaptive features, as exemplified by PCA coefficients, Haar wavelets, and local receptive fields (LRFs). In terms of classifiers, we consider the popular Support Vector Machines (SVMs), feed-forward neural networks, and k-nearest neighbor classifier. Experiments are performed on a large data set consisting of 4,000 pedestrian and more than 25,000 nonpedestrian (labeled) images captured in outdoor urban environments. Statistically meaningful results are obtained by analyzing performance variances caused by varying training and test sets. Furthermore, we investigate how classification performance and training sample size are correlated. Sample size is adjusted by increasing the number of manually labeled training data or by employing automatic bootstrapping or cascade techniques. Our experiments show that the novel combination of SVMs with LRF features performs best. A boosted cascade of Haar wavelets can, however, reach quite competitive results, at a fraction of computational cost. The data set used in this paper is made public, establishing a benchmark for this important problem.
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
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
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
Statistical pattern recognition: a review
Jain, A. K., Duin, P. W., & Mao, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824819 · Cited by 6,767
A desicion-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1995) · Lecture Notes in Computer Science · doi:10.1007/3-540-59119-2_166 · Cited by 2,252
Detecting Pedestrians Using Patterns of Motion and Appearance
Viola, P., Jones, M. J., & Snow, D. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-6644-8 · Cited by 2,079
Distance transformations in digital images
Borgefors, G. (1986) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(86)80047-0 · Cited by 1,898
The Visual Analysis of Human Movement: A Survey
Gavrila, D. M. (1999) · Computer Vision and Image Understanding · doi:10.1006/cviu.1998.0716 · Cited by 1,782
Self-citationExample-based learning for view-based human face detection
Sung, K.-K., & Poggio, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655648 · Cited by 1,773
A Trainable System for Object Detection
Papageorgiou, C., & Poggio, T. (2000) · International Journal of Computer Vision · doi:10.1023/a:1008162616689 · Cited by 1,289
Cited by
Pedestrian Detection: An Evaluation of the State of the Art
Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.155 · Cited by 3,190
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
Non-contact, automated cardiac pulse measurements using video imaging and blind source separation
Poh, M.-Z., McDuff, D. J., & Picard, R. W. (2010) · Optics Express · doi:10.1364/oe.18.010762 · Cited by 1,643
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
Monocular Pedestrian Detection: Survey and Experiments
Enzweiler, M., & Gavrila, D. M. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.260 · Cited by 1,213
Self-citationClassification using intersection kernel support vector machines is efficient
Maji, S., Berg, A. C., & Malik, J. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587630 · Cited by 940
Vehicle detection in aerial imagery : A small target detection benchmark
Razakarivony, S., & Jurie, F. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.11.002 · Cited by 916
Efficient Additive Kernels via Explicit Feature Maps
Vedaldi, A., & Zisserman, A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.153 · Cited by 688
EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes
Braun, M., Krebs, S., Flohr, F., & Gavrila, D. M. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2897684 · Cited by 307
Self-citationHuman detection from images and videos: A survey
Nguyen, D. T., Li, W., & Ogunbona, P. O. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2015.08.027 · Cited by 267
Single-Pedestrian Detection Aided by Multi-pedestrian Detection
Ouyang, W., & Wang, X. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.411 · Cited by 144
Transfer learning for pedestrian detection
Cao, X., Wang, Z., Yan, P., & Li, X. (2013) · Neurocomputing · doi:10.1016/j.neucom.2011.12.043 · Cited by 57
Pedestrian detection in automotive safety: Understanding state-of-the-art
Ragesh, N. K., & Rajesh, R. (2019) · IEEE Access · doi:10.1109/access.2019.2909992 · Cited by 57
Review of pedestrian detection techniques in automotive far‐infrared video
Hurney, P., Waldron, P., Morgan, F., Jones, E., & Glavin, M. (2015) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2014.0236 · Cited by 51
Extending and Analyzing Self-supervised Learning Across Domains
Wallace, B., & Hariharan, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_43 · Cited by 35
Automated cyclist data collection under high density conditions
Zaki, M. H., & Sayed, T. (2016) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2014.0257 · Cited by 11
Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases
Ortiz Castelló, V., del Tejo Catalá, O., Salvador Igual, I., & Perez-Cortes, J.-C. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420929175 · Cited by 7
Metadata
- Authors
- Munder, S., & Gavrila, D. M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-11
- Volume
- 28
- Issue
- 11
- Pages
- 1863-1868
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2154740476
- Cited by
- 555
- References
- 19
- FWCI
- 53.71
- Type
- journal article
- 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
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.
