Computer Vision
 
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Person Re-Identification

510 papers with code • 34 benchmarks • 57 datasets

Person Re-Identification is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.

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

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Most implemented papers

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Bootstrap your own latent: A new approach to self-supervised Learning

deepmind/deepmind-research • • 13 Jun 2020

From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.

31
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Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)

huanghoujing/person-reid-triplet-loss-baseline • • ECCV 2018

RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency.

29
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Person Transfer GAN to Bridge Domain Gap for Person Re-Identification

yxgeee/MMT • • CVPR 2018

Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e. g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network.

25
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Random Erasing Data Augmentation

zhunzhong07/Random-Erasing • • 16 Aug 2017

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).

18
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Learning Discriminative Features with Multiple Granularities for Person Re-Identification

seathiefwang/MGN-pytorch • • 4 Apr 2018

Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities.

16
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Omni-Scale Feature Learning for Person Re-Identification

KaiyangZhou/deep-person-reid • • ICCV 2019

As an instance-level recognition problem, person re-identification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales.

16
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AlignedReID: Surpassing Human-Level Performance in Person Re-Identification

huanghoujing/AlignedReID-Re-Production-Pytorch • • 22 Nov 2017

In this paper, we propose a novel method called AlignedReID that extracts a global feature which is jointly learned with local features.

15
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Joint Discriminative and Generative Learning for Person Re-identification

layumi/Person_reID_baseline_pytorch • • CVPR 2019

To this end, we propose a joint learning framework that couples re-id learning and data generation end-to-end.

12
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Circle Loss: A Unified Perspective of Pair Similarity Optimization

layumi/Person_reID_baseline_pytorch • • CVPR 2020

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.

12
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Camera Style Adaptation for Person Re-identification

zhunzhong07/CamStyle • • CVPR 2018

In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation.

10
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