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.
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In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
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
With LSR, we demonstrate consistent improvement in all systems regardless of the extent of over-fitting.
#17 best model for Person Re-Identification on DukeMTMC-reID
RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency.
#11 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.
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
The present study collects and evaluates these effective training tricks in person ReID.
In the literature, some effective training tricks are briefly appeared in several papers or source codes.
#2 best model for Person Re-Identification on Market-1501
In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning.
#20 best model for Person Re-Identification on Market-1501
Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates.
#27 best model for Person Re-Identification on DukeMTMC-reID