The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification
Abstract
In this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.
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Going deeper with convolutions
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Stacked Hourglass Networks for Human Pose Estimation
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Scalable Person Re-identification: A Benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.133 · Cited by 4,506
Self-citationWei, S.-E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.511 · Cited by 2,966
Person re-identification by Local Maximal Occurrence representation and metric learning
Liao, S., Hu, Y., Zhu, X., & Li, S. Z. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298832 · Cited by 2,091
Stacked Attention Networks for Image Question Answering
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Large scale metric learning from equivalence constraints
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Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
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Cited by
Tang, 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
Self-citationVERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild
Lou, Y., Bai, Y., Liu, J., Wang, S., & Duan, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00335 · Cited by 326
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Metadata
- Authors
- Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J., Yan, J., Wang, S., Li, H., & Wang, X.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 379-387
- OpenAlex
- W2776879428
- Cited by
- 366
- References
- 28
- FWCI
- 11.33
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J., Yan, J., Wang, S., Li, H., & Wang, X. (2017). Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.49
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