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 )


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

Most implemented papers

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.

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.

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.

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).

VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification

layumi/Person_reID_baseline_pytorch 14 Apr 2020

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

Cluster Contrast for Unsupervised Person Re-Identification

alibaba/cluster-contrast 22 Mar 2021

Thus, our method can solve the problem of cluster inconsistency and be applicable to larger data sets.

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.

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.

Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough

interestingzhuo/pnploss 9 Feb 2021

Limited by the definition of AP, such methods consider both negative and positive instances ranking before each positive instance.