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Code for our ICCV 2017 paper -- Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification
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data Init Aug 4, 2017
datasets Init Aug 4, 2017
layers Init Aug 4, 2017
models update to fix bug of fixed bug........ Nov 7, 2017
README.md Updataefor training Aug 5, 2017
reIdEval.lua add eval Aug 5, 2017
reIdTrain.lua
test.lua Init Aug 4, 2017
train.lua Init Aug 4, 2017

README.md

Spatial-Temporal-Pooling-Networks-ReID

Code for our ICCV 2017 paper -- Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification

If you use this code please cite:

@inproceedings{shuangjiejointly,
  	title={Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification},
  	author={Shuangjie Xu, Yu Cheng, Kang Gu, Yang Yang, Shiyu Chang and Pan Zhou},
  	booktitle={ICCV},
  	year={2017}
}

Dependencies

The following libaries are necessary:

  • torch and its package (nn, nnx, optim, cunn, cutorch, image, rnn , inn). Installation guide
  • CUDA support with Nvidia GPU
  • Matlab for data preparation

Data Preparation

Download and extract datasets iLIDS-VID, PRID2011 and MARS into the data/ directory. data/iLIDS-VID for example.

Modify and run data/computeOpticalFlow.m with Matlab to generate Optical Flow data. Optical Flow data will be generated in the same dir of your datasets. data/iLIDS-VID-OF-HVP for example.

MARS needs some extra codes to randomly choose two videos for a person (cam1 and cam2). Will release soon.

Evaluation

You can evaluate this network without training. Download weights files for iLIDS-VID or PRID2011 (baidu yun or google drive) into weights/, and run

th reIdEval.lua -dataset 1 -weight /weights/ilids_600_convnet.dat
th reIdEval.lua -dataset 2 -weight /weights/prid_600_convnet.dat

dataset 1 for iLIDS-VID, 2 for PRID2011 and 3 for MARS (release soon)

Training

Run this command for training iLIDS-VID. To train other datasets, change options -dataset.

th reIdTrain.lua -nEpochs 600 -dataset 1 -dropoutFrac 0.6 -sampleSeqLength 16 -samplingEpochs 100 -seed 1 -mode 'spatial_temporal'

The option mode has four type: cnn-rnn, spatial, temporal and spatial_temporal.

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