Proceedings ArticleDOI
Combination of Appearance and License Plate Features for Vehicle Re-Identification
Yanguang He,Chenhe Dong,Ying Wei +2 more
- pp 3108-3112
TLDR
The experimental results show that the proposed two-module framework outperforms most state-of-the-art approaches for vehicle Re-ID, even if only the appearance module is used.Abstract:
In this work, we propose a two-module framework that combines appearance and corresponding license plate features for vehicle re-identification (Re-ID). In the appearance module, we design a Two-Branch Network to extract comprehensive global features. To obtain more discriminative feature representations, we propose an enhanced triplet loss (ETL) and combine ETL with softmax loss to optimize the parameter of Two-Branch Network. In the license plate module, we present a license plate Re-ID network that incorporates the bidirectional LSTMs into CNNs, which is effective for capturing the contexts in license plate images and significantly improves the performance of license plate Re-ID. We validate our method on both VeRi-776 dataset [1] and VehicleID dataset [2]. The experimental results show that our method outperforms most state-of-the-art approaches for vehicle Re-ID, even if only the appearance module is used.read more
Citations
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Journal ArticleDOI
Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-Identification
TL;DR: Extensive experiments on three large scale vehicle databases demonstrate that the proposed SGN is superior to state-of-the-art vehicle re-identification approaches and a novel pyramidal graph network is designed to comprehensively explore the spatial significance of feature maps at multiple scales.
Journal ArticleDOI
Multi-attribute adaptive aggregation transformer for vehicle re-identification
TL;DR: Zhang et al. as mentioned in this paper proposed a vehicle attribute transformer (VAT) for vehicle re-identification, which considers color and model as the most intuitive attributes of the vehicle, the vehicle colour and model are relatively stable and easy to distinguish.
Journal ArticleDOI
Viewpoint Adaptation Learning with Cross-view Distance Metric for Robust Vehicle Re-Identification
TL;DR: Results of extensive experiments on two large scale vehicle Re- ID datasets, namely, VeRi-776 and VehiclelD demonstrate that the performance of the proposed VANet with a cross-view distance metric is robust and superior to other state-of-the-art Re-ID methods across multiple viewpoints.
Proceedings ArticleDOI
HSGM: A Hierarchical Similarity Graph Module for Object Re-Identification
TL;DR: Wang et al. as discussed by the authors proposed a hierarchical similarity graph module (HSGM) to relieve the conflict of backbone networks and mine the discriminative features, which constructs a rich hierarchical graph to explore the pairwise relationships among global-local and local-local.
Journal ArticleDOI
LABNet: Local graph aggregation network with class balanced loss for vehicle re-identification
TL;DR: A local graph aggregation network that considers spatial regions of the feature map as nodes and builds a local neighborhood graph that performs local feature aggregation before the global average pooling layer to improve feature learning as well as reduce the effects of partial occlusion and background clutter.
References
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