The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Vehicle Type Classification Using a Semisupervised Convolutional Neural Network
Abstract
In this paper, we propose a vehicle type classification method using a semisupervised convolutional neural network from vehicle frontal-view images. In order to capture rich and discriminative information of vehicles, we introduce sparse Laplacian filter learning to obtain the filters of the network with large amounts of unlabeled data. Serving as the output layer of the network, the softmax classifier is trained by multitask learning with small amounts of labeled data. For a given vehicle image, the network can provide the probability of each type to which the vehicle belongs. Unlike traditional methods by using handcrafted visual features, our method is able to automatically learn good features for the classification task. The learned features are discriminative enough to work well in complex scenes. We build the challenging BIT-Vehicle dataset, including 9850 high-resolution vehicle frontal-view images. Experimental results on our own dataset and a public dataset demonstrate the effectiveness of the proposed method.
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
“In comparison to these two approaches, recent DL-based solutions such as [2], [3], [23], [24] have been widely adopted due to their robustness and improved vehicle detection performance in images and videos.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [23]
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
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
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
Research works printed but not linked: 35
- 10.2307/2006193 · DOI
- 10.1080/net.12.3.255.270 · DOI
- 10.1016/0031-3203(82)90024-3 · DOI
- kumar, 2012, Learning task grouping and overlap in multi-task learning, Proc Int Conf Mach Learn, 1383
- belkin, 2006, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples, J Mach Learn Res, 7, 2399
- 10.1162/neco.1994.6.4.559 · DOI
- ngiam, 0, Sparse filtering, Proc Adv Neural Inf Process Syst, 1125
- 10.1371/journal.pcbi.0040027 · DOI
- lyu, 0, Nonlinear image representation using divisive normalization, Proc IEEE Comput Soc Conf Comput Vis Pattern Recog, 1
- sobel, 1970, Camera Models and Machine Perception
- 10.1137/080716542 · DOI · Paper page
- 10.5244/C.18.61 · DOI
- 10.1109/ICPR.2006.264 · DOI
- 10.1016/j.csi.2010.06.005 · DOI
- 10.1109/TITS.2012.2213814 · DOI
- 10.1007/978-1-4614-3501-3_27 · DOI
- 10.1109/ICPR.2002.1048232 · DOI
- osadchy, 2007, Synergistic face detection and pose estimation with energy-based models, J Mach Learn Res, 8, 1197
- 10.1109/TIP.2011.2160954 · DOI
- 10.1109/TITS.2006.874722 · DOI
- zhang, 0, A PCA-based vehicle classification framework, Proc 22nd IEEE Intl Conf Data Engineering, 17
- kavukcuoglu, 0, Learning convolutional feature hierarchies for visual recognition, Proc Adv Neural Inf Process Syst, 1090
- ma, 0, Edge-based rich representation for vehicle classification, Proc IEEE Int Conf Comput Vis, 2, 1185
- 10.1109/TPAMI.2007.70728 · DOI
- 10.1109/ICAL.2007.4338720 · DOI
- 10.1109/6979.994794 · DOI
- 10.1007/978-3-319-01766-2_68 · DOI
- 10.1109/ITSC.2001.948656 · DOI
- 10.1109/CVPR.2013.446 · DOI
- 10.1109/CVPR.2014.212 · DOI · Paper page
- 10.1109/CVPR.2013.465 · DOI
- zhen, 0, Vehicle type classification using distributions of structural and appearance-based features, Proc Int Conf Image Process, 4321
- 10.1109/CVPR.2014.224 · DOI
- 10.1109/ICMEW.2012.73 · DOI
- 10.1126/science.1127647 · DOI · Paper page
Cited by
Sorted by citations
Drone-Based RGB-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning
Sun, Y., Cao, B., Zhu, P., & Hu, Q. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3168279 · Cited by 790
“Vehicle detection aims to find the location of the vehicle and determine its category [39].”
Convolutional Neural Networks: A Survey
Krichen, M. (2023) · Computers · doi:10.3390/computers12080151 · Cited by 623
Joint deep learning for land cover and land use classification
Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.11.014 · Cited by 451
“…117 feature representation, the CNN has demonstrated great potential in many remotely sensed 118 tasks such as vehicle detection (Chen et al., 2014; Dong et al., 2015), road network extraction 119 (Cheng et al., 2017), remotely sensed scene classification (Othman et al., 2016), and semantic 120…”
BackgroundReal-Time Vehicle Detection Based on Improved YOLO v5
Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., & Guo, X. (2022) · Sustainability · doi:10.3390/su141912274 · Cited by 503
“A total of 1493 pictures with vehicle information were extracted, and the original vehicle detection image library was set with the selection of 1060 pictures of multiple types of vehicles from the BIT Vehicle_Dataset [33].”
MethodAn object-based convolutional neural network (OCNN) for urban land use classification
Zhang, C., Sargent, I. M. J., Pan, X., Li, H., Gardiner, A., Hare, J. S., & Atkinson, P. M. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.06.034 · Cited by 452
“…and scene understanding, the CNN has demonstrated great potential in 99 many remote sensing tasks such as vehicle detection (Chen et al., 2014; Dong et al., 2015), 100 road network extraction (Cheng et al., 2017), remotely sensed scene classification (Othman et 101 al., 2016; Sargent et al.,…”
Background
Metadata
- Authors
- Dong, Z., Wu, Y., Pei, M., & Jia, Y.
- Affiliations
- Beijing Institute of Technology (CN)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2015-08
- Volume
- 16
- Issue
- 4
- Pages
- 2247-2256
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2045721916
- Cited by
- 387
- References
- 0
- Citations by year
- 2026: 16 · 2025: 27 · 2024: 41 · 2023: 41 · 2022: 59 · 2021: 52 · 2020: 45 · 2019: 33 · 2018: 34 · 2017: 26 · 2016: 12 · 2015: 1
- FWCI
- 21.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China (61203291)
- Specialized Research Fund for the Doctoral Program of Higher Education of China (20121101120029)
- Specialized Fund for Joint Building Program of Beijing Municipal Education Commission
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- vehicle classification, semi-supervised learning, convolutional neural networks, unlabeled data, multi-task learning, representation learning
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
