The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Deep Relative Distance Learning: Tell the Difference between Similar Vehicles
Abstract
The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This paper focuses on an interesting but challenging problem, vehicle re-identification (a.k.a precise vehicle search). We propose a Deep Relative Distance Learning (DRDL) method which exploits a two-branch deep convolutional network to project raw vehicle images into an Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. To further facilitate the future research on this problem, we also present a carefully-organized largescale image database "VehicleID", which includes multiple images of the same vehicle captured by different realworld cameras in a city. We evaluate our DRDL method on our VehicleID dataset and another recently-released vehicle model classification dataset "CompCars" in three sets of experiments: vehicle re-identification, vehicle model verification and vehicle retrieval. Experimental results show that our method can achieve promising results and outperforms several state-of-the-art approaches.
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 3,752
3D Object Representations for Fine-Grained Categorization
Krause, J., Stark, M., Deng, J., & Fei-Fei, L. (2013) · 2013 IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.77 · Cited by 3,496
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335
Person re-identification by symmetry-driven accumulation of local features
Farenzena, M., Bazzani, L., Perina, A., Murino, V., & Cristani, M. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539926 · Cited by 1,499
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
Gray, D., & Tao, H. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_21 · Cited by 1,466
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461
An improved deep learning architecture for person re-identification
Ahmed, E., Jones, M., & Marks, T. K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299016 · Cited by 1,248
Evaluating Appearance Models for Recognition, Reacquisition, and Tracking
Gray, D. A., Brennan, S., & Hai, T. (2007) · Cited by 984
A large-scale car dataset for fine-grained categorization and verification
Yang, L., Luo, P., Loy, C. C., & Tang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299023 · Cited by 825
Deep feature learning with relative distance comparison for person re-identification
Ding, S., Lin, L., Wang, G., & Chao, H. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.005 · Cited by 684
Person Re-identification by Video Ranking
Wang, T., Gong, S., Zhu, X., & Wang, S. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10593-2_45 · Cited by 642
Learning Mid-level Filters for Person Re-identification
Zhao, R., Ouyang, W., & Wang, X. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.26 · Cited by 490
Learning Locally-Adaptive Decision Functions for Person Verification
Li, Z., Chang, S., Liang, F., Huang, T. S., Cao, L., & Smith, J. R. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.463 · Cited by 466
Self-citationBayesian Face Revisited: A Joint Formulation
Chen, D., Cao, X., Wang, L., Wen, F., & Sun, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_41 · Cited by 427
Deep Metric Learning for Practical Person Re-IdentificationPreprint
Yi, D., Lei, Z., & Li, S. Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.4979 · Cited by 151
Jointly Optimizing 3D Model Fitting and Fine-Grained Classification
Lin, Y.-L., Morariu, V. I., Hsu, W. H., & Davis, L. S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_31 · Cited by 117
Three-Dimensional Deformable-Model-Based Localization and Recognition of Road Vehicles
Zhang, Z., Tan, T., Huang, K., & Wang, Y. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2160954 · Cited by 103
Self-citationVehicle Reidentification Using Multidetector Fusion
Sun, C. C., Arr, G. S., Ramachandran, R. P., & Ritchie, S. G. (2004) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2004.833770 · Cited by 61
Car make and model recognition using 3D curve alignment
Hsiao, E., Sinha, S. N., Ramnath, K., Baker, S., Zitnick, L., & Szeliski, R. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836126 · Cited by 54
Car make and model recognition using 3D curve alignment
Ramnath, K., Sinha, S. N., Szeliski, R., & Hsiao, E. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836087 · Cited by 50
Cited by
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503
TransReID: Transformer-based Object Re-Identification
He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01474 · Cited by 1,244
Perceptual Generative Adversarial Networks for Small Object Detection
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., & Yan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.211 · Cited by 953
“For traffic sign detection, we use the pretrained VGGCNN-M-1024 model [3] as adopted in [24] to initialize our network.”
MethodTang, Z., Naphade, M., Liu, M.-Y., Yang, X., Birchfield, S., Wang, S., Kumar, R., Anastasiu, D., & Hwang, J.-N. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00900 · Cited by 454
Metadata
- Authors
- Liu, H., Tian, Y., Wang, Y., Pang, L., & Huang, T.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 2167-2175
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2470322391
- Cited by
- 782
- References
- 25
- FWCI
- 21.38
- 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
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.
