Cross-View Gait Based Human Idendification with Deep CNNs

A pytorch implementation of the Local-Bottom Network (LB) in the paper:

Wu, Zifeng, et al. "A comprehensive study on cross-view gait based human identification with deep cnns." IEEE transactions on pattern analysis and machine intelligence 39.2 (2017): 209-226.

Dependency

python3
pytorch >= 0.4.0
visdom.
opencv

Model

In src/model.py there are two models: LBNet and LBNet_1. LBNet_1 is more close to the model described in the section 4.2.1 of the original paper. You can select either one. The results are close to each other.

Training

To train the model, put the CASIA-B dataset silhoutte data under repository
mkdir snapshot to build the directory for saving models
goto the src dir and run

python3 train.py

The model will be saved into the execution dir every 10000 iterations. You can change the interval in train.py.

Monitor the performance

Install visdom.
Start the visdom server with python3 -m visdom.server -port 5274 or any port you like (change the port in train.py and test.py)
Open this URL in your browser: http://localhost:5274 You will see the training loss curve and the validation accuracy curve.

Testing

goto src dir and run python3 test.py. You can select which snapshot to use by modifying the checkpoint = th.load('../snapshot/snapshot_75000.pth') to other snapshots. Be patient since it takes a long time. The computed similarities will be saved into similarity.npy
run python3 compute_acc_per_angle to compute the accuracy for each prove view and gallery view. The results will be saved into acc_table.csv

You will get a table like this.