Vehicle Re-Identification
53 papers with code • 12 benchmarks • 9 datasets
Vehicle re-identification is the task of identifying the same vehicle across multiple cameras.
( Image credit: A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras )
Benchmarks
These leaderboards are used to track progress in Vehicle Re-Identification
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Libraries
Use these libraries to find Vehicle Re-Identification models and implementationsDatasets
VehicleID
VeRi-776
CityFlow
VeRi-Wild
VehicleX
VRAI
Vehicle-Rear
Vehicle-1M
Car datasets in multiple scenes
Most implemented papers
VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera
Xiangyu-CAS/AICity2020-VOC-ReID
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Then we use orientation and camera similarity as penalty to get final similarity.
Vehicle Re-Identification Based on Complementary Features
Top performance in City-Scale Multi-Camera Vehicle Re-Identification demonstrated the advantage of our methods, and we got 5-th place in the vehicle Re-ID track of AIC2020.
Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
muzishen/HPGN
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Secondly, a novel pyramidal graph network (PGN) is designed to comprehensively explore the spatial significance of feature maps at multiple scales.
Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond
WangWenhao0716/Attentive-WaveBlock
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This paper proposes a novel light-weight module, the Attentive WaveBlock (AWB), which can be integrated into the dual networks of mutual learning to enhance the complementarity and further depress noise in the pseudo-labels.
Robust Re-Identification by Multiple Views Knowledge Distillation
To achieve robustness in Re-Identification, standard methods leverage tracking information in a Video-To-Video fashion.
Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network
tsaishien-chen/SPAN
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ECCV 2020
Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras.
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond
ming1993li/self-supervised-geometric
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ICCV 2021
To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors.
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng-Zhang/gnn-re-ranking
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We argue that the first phase equals building the k-nearest neighbor graph, while the second phase can be viewed as spreading the message within the graph.
Viewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification
Moreover, our method also achieves competitive performance compared with recent works on existing vehicle ReID datasets including VehicleID, VeRi-776 and VERI-Wild.
Unsupervised Vehicle Re-Identification via Self-supervised Metric Learning using Feature Dictionary
The results of DPLM are applied to dictionary-based triplet loss (DTL) to improve the discriminativeness of learnt features and to refine the quality of the results of DPLM progressively.
