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computer-vision-1

Vehicle Re-Identification

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in vehicle-re-identification-2

Trend

Dataset

Best Model

Actions

VeRi-776

VeRi-776

VehicleID Small

VehicleID Small

VehicleID Large

Libraries

i

Use these libraries to find vehicle-re-identification-2 models and implementations

layumi/Person_reID_baseline_pytorch

2 papers 3,975

Datasets

VehicleID

VeRi-776

CityFlow

VeRi-Wild

VehicleX

VRAI

Subtasks

No subtasks available.

Most implemented papers

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

Shuo Wang, M. Naphade, Zheng Tang, Xiaodong Yang, Stan Birchfield, Ratnesh Kumar, Jonathan Tremblay, William Hodge•Mon Sep 30 2019

A Pose-Aware Multi-Task Re-Identification (PAMTRI) framework that overcomes viewpoint-dependency by explicitly reasoning about vehicle pose and shape via keypoints, heatmaps and segments from pose estimation and achieves significant improvement over state-of-the-art on two mainstream vehicle ReID benchmarks.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

VehicleID Large

VehicleID Medium

VehicleID Medium

VeRi-Wild Small

VeRi-Wild Small

VeRi

VeRi

VeRi-Wild Large

VeRi-Wild Large

CityFlow

CityFlow

VeRi-Wild Medium

VeRi-Wild Medium

VehicleID

VehicleID

VRAI test

VRAI test

VRAI test-dev

VRAI test-dev

Vehicle-Rear

Vehicle-Rear

Vehicle-1M

Car datasets in multiple scenes

Car datasets in multiple scenes

145

0

VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification

Yunchao Wei, Zhedong Zheng, Yezhou Yang, Tao Ruan•Mon Dec 31 2018

This paper presents the solution to AICity Vehicle Re-id Challenge 2019, and builds a large-scale vehicle dataset called VehicleNet upon the public web data using the free data from the web and deploying the two-stage learning strategy.

31 0

FastReID: A Pytorch Toolbox for General Instance Re-identification

Xinchen Liu, Wu Liu, Tao Mei, Lingxiao He, Xingyu Liao, Peng Cheng•Wed Jun 03 2020

This work presents FastReID as a widely used software system that supports single and multiple GPU servers, it can reproduce the project results very easily, and has implemented some state-of-the-art projects, including person re-id, partial re-ids, cross-domain re-ID, and vehicle re-identification.

386 0

TransReID: Transformer-based Object Re-Identification

F. Wang, Hao Li, Shuting He, Haowen Luo, Wei Jiang, Pichao Wang•Sun Feb 07 2021

This work first encode an image as a sequence of patches and build a transformer-based strong baseline with a few critical improvements, which achieves competitive results on several ReID benchmarks with CNN-based methods.

1085 0

VehicleNet: Learning Robust Visual Representation for Vehicle Re-Identification

Yunchao Wei, Zhedong Zheng, Tao Mei, Yi Yang, Tao Ruan•Mon Apr 13 2020

This work proposes to build a unique large-scale vehicle dataset by harnessing four public vehicle datasets, and design a simple yet effective two-stage progressive approach to learning more robust visual representation from VehicleNet.

170 0

Cluster Contrast for Unsupervised Person Re-Identification

P. Tan, Siyu Zhu, Zuozhuo Dai, Guangyuan Wang, Weihao Yuan•Sun Mar 21 2021

Cluster Contrast employs a unique cluster representation to describe each cluster, resulting in a cluster-level memory dictionary, which can solve the problem of cluster inconsistency and be applicable to larger data sets.

281 0

Simulating Content Consistent Vehicle Datasets with Attribute Descent

M. Naphade, Yue Yao, Xiaodong Yang, Liang Zheng, Tom Gedeon•Tue Dec 17 2019

This paper introduces a large-scale synthetic dataset VehicleX, which contains 1,362 vehicles of various 3D models with fully editable attributes, and proposes an attribute descent approach to let VehicleX approximate the attributes in real-world datasets.

134 0

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

F. Wang, Yuqi Zhang, Shuting He, Hao Luo, Wei Jiang, Weihua Chen, Miao Zhang, Hao Li•Tue Apr 21 2020

The Identity Mining method to automatically generate pseudo labels for a part of the testing data, which is better than the k-means clustering, is proposed and achieves third place in the AICITY20 competition.

74 0

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

Andrew Zisserman, Weidi Xie, Vicky Kalogeiton, A. Brown•Wed Jul 22 2020

Smooth-AP is a plug-and-play objective function that allows for end-to-end training of deep networks with a simple and elegant implementation and improves the performance over the state-of-the-art, especially for larger-scale datasets, thus demonstrating the effectiveness and scalability of Smooth-AP to real-world scenarios.

181 0

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

Weiqing Min, Xiaoming Wei, Shuqiang Jiang, Xiaolin Wei, Zhu Li, Jiajun Song, Yaohui Zhu, Liping Kang•Mon Feb 08 2021

This work proposes a novel loss, namely Penalizing Negative instances before Positive ones (PNP), which can directly minimize the number of negative instances before each positive one and systematically investigates different gradient assignment solutions via constructing derivative functions of the loss.

11 0

Adding a benchmark result helps the community track progress.

Vehicle Re-Identification | State-of-the-Art