Person re-identification is the task of associating images of the same person taken from different cameras or from the same camera in different occasions.
( Image credit: PRID2011 dataset )
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In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
Person re-identification (re-ID), which aims to re-identify people across different camera views, has been significantly advanced by deep learning in recent years, particularly with convolutional neural networks (CNNs).
An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation.
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.
#2 best model for Person Re-Identification on CUHK03
To this end, we propose a joint learning framework that couples re-id learning and data generation end-to-end.
#3 best model for Person Re-Identification on CUHK03
RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency.
#12 best model for Person Re-Identification on DukeMTMC-reID
Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms.
The present study collects and evaluates these effective training tricks in person ReID.