cocosister/cnn-rnn2016

Reimplementation the paper of Recurrent Convolutional Network for Video-based Person Re-Identification in Pytorch

CNN-RNN2016

Reimplementation the paper of Recurrent Convolutional Network for Video-based Person Re-Identification in Pytorch

Preparation

Python 3.6
Pytorch >= 0.4.0

Result on Prid2011

版本	map	rank1	rank5	rank10	rank20
复现	58.8%	49.4%	68.5%	83.1%	89.9%
原文	--	70%	90%	95%	97%

Problems

P.1 use the official split to form dataset, the dataset is too small.

train identites: 89, test identites: 89

=> PRID-2011 loaded

subset	# ids	# tracklets
train	89	178
query	89	89
gallery	89	89
total	178	356

P.2 Data Augmentation

1.mirror is not implemented
2.resize the image size from (128, 64) to (256, 128),not same as (64,48) in offical code

P.3 Training

1.If batch-size set to 1, the net will not be convergent.
2.The dataset is too small, we can change the dataset generation way to extend 
  the dataset. Maybe like the paper 'Video Person Re-identification with
  Competitive Snippet-similarity Aggregation and Co-attentive Snippet Embedding'.

Reference

Recurrent-Convolutional-Video-ReID

Spatial-Temporal-Pooling-Networks-ReID