Proceedings ArticleDOI
Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification
Zhongdao Wang,Luming Tang,Xihui Liu,Zhuliang Yao,Shuai Yi,Jing Shao,Junjie Yan,Shengjin Wang,Hongsheng Li,Xiaogang Wang +9 more
- pp 379-387
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TLDR
Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements in the vehicle Re-identification problem.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.read more
Citations
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Proceedings ArticleDOI
Local-guided Global Collaborative Learning Transformer for Vehicle Reidentification
TL;DR: Li et al. as mentioned in this paper proposed a global collaborative learning Transformer guided by local abstract features, which aims to highlight the highest-attention regions of vehicle images, and adopted Vision Transformer(ViT) as their backbone to extract global features and obtain all local tokens.
Proceedings ArticleDOI
A Vehicle Re-Identification Method Based on Fine-Grained Features and Metric Learning
He Yan,Xiaotang Wang +1 more
TL;DR: In this paper , the fine-grained features of vehicles are extracted by using triplet constraints, and then combined with the global features extracted by the backbone network as vehicle features.
Journal ArticleDOI
Bi-Level Implicit Semantic Data Augmentation for Vehicle Re-Identification
TL;DR: Zhang et al. as mentioned in this paper proposed the Bi-level Implicit Semantic Data Augmentation (BIDA) framework to enhance the robustness of Re-ID models by augmenting the images semantically in the feature space according to the identity-level and superclass-level intra-class variations.
Proceedings ArticleDOI
Vehicle re-identification method based on semantic information enhancement and feature complementarity guided by keypoints
TL;DR: Zhang et al. as mentioned in this paper proposed a vehicle re-ID method based on keypoint-guided semantic feature alignment and graph matching strategy to enhance complementary information under the transformer framework.
Journal ArticleDOI
Multiple Soft Attention Network for Vehicle Re-Identification
TL;DR: In this article , the authors proposed a multiple soft attention network to provide part-aware attention weights and extract more representative and robust features for vehicle ReID, which achieved state-of-the-art performance among the approaches that did not use metadata.
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