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
Recall@k Surrogate Loss with Large Batches and Similarity Mixup
yash0307/RecallatK_surrogate
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CVPR 2022
This work focuses on learning deep visual representation models for retrieval by exploring the interplay between a new loss function, the batch size, and a new regularization approach.
PP-ShiTu: A Practical Lightweight Image Recognition System
In recent years, image recognition applications have developed rapidly.
A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras
icarofua/siamese-two-stream
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We describe in this paper a Two-Stream Siamese Neural Network for vehicle re-identification.
Variational Representation Learning for Vehicle Re-Identification
saghiralfasly/VFL-Vehicle-Re-Id
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One of the most challenging problems is to learn an efficient representation for a vehicle from its multi-viewpoint images.
A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification
Pirazh/Vehicle_Key_Point_Orientation_Estimation
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ICCV 2019
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
Part-Guided Attention Learning for Vehicle Instance Retrieval
zhangxinyu-xyz/PGAN-VehicleRe-ID
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Finally, we aggregate the global appearance and part features to improve the feature performance further.
Deep Meta Metric Learning
In this paper, we present a deep meta metric learning (DMML) approach for visual recognition.
Improving Vehicle Re-Identification using CNN Latent Spaces: Metrics Comparison and Track-to-track Extension
GeoTrouvetout/Vehicle_ReID
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We compared T2TP with I2TP using the same CNN models.
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate.
Parsing-based View-aware Embedding Network for Vehicle Re-Identification
silverbulletmdc/PVEN
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CVPR 2020
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario.
