Vehicle Re-Identification

36 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 )


Use these libraries to find Vehicle Re-Identification models and implementations

Most implemented papers

VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification

layumi/Person_reID_baseline_pytorch 7 Aug 2020

This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.

TransReID: Transformer-based Object Re-Identification

heshuting555/TransReID ICCV 2021

Extracting robust feature representation is one of the key challenges in object re-identification (ReID).

Simulating Content Consistent Vehicle Datasets with Attribute Descent

yorkeyao/VehicleX ECCV 2020

Between synthetic and real data, there is a two-level domain gap, i. e., content level and appearance level.

Multi-Domain Learning and Identity Mining for Vehicle Re-Identification

heshuting555/AICITY2020_DMT_VehicleReID 22 Apr 2020

Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.

PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data


In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention.

FastReID: A Pytorch Toolbox for General Instance Re-identification

JDAI-CV/fast-reid 4 Jun 2020

General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research.

Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval

Andrew-Brown1/Smooth_AP ECCV 2020

Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods.

Recall@k Surrogate Loss with Large Batches and Similarity Mixup

yash0307/RecallatK_surrogate 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

PaddlePaddle/PaddleClas 1 Nov 2021

In recent years, image recognition applications have developed rapidly.

007: Democratically Finding The Cause of Packet Drops

behnazak/Vigil-007SourceCode 20 Feb 2018

Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur.