GaitSet

GaitSet is a flexible, effective and fast network for cross-view gait recognition. The paper has been published on IEEE TPAMI.

Flexible

The input of GaitSet is a set of silhouettes.

There are NOT ANY constrains on an input, which means it can contain any number of non-consecutive silhouettes filmed under different viewpoints with different walking conditions.
As the input is a set, the permutation of the elements in the input will NOT change the output at all.

Effective

It achieves Rank@1=95.0% on CASIA-B and Rank@1=87.1% on OU-MVLP, excluding identical-view cases.

Fast

With 8 NVIDIA 1080TI GPUs, it only takes 7 minutes to conduct an evaluation on OU-MVLP which contains 133,780 sequences and average 70 frames per sequence.

What's new

The code and checkpoint for OUMVLP dataset have been released. See OUMVLP for details.

Prerequisites

Python 3.6
PyTorch 0.4+
GPU

Getting started

Installation

(Not necessary) Install Anaconda3
Install CUDA 9.0
install cuDNN7.0
Install PyTorch

Noted that our code is tested based on PyTorch 0.4

Dataset & Preparation

Download CASIA-B Dataset

!!! ATTENTION !!! ATTENTION !!! ATTENTION !!!

Before training or test, please make sure you have prepared the dataset by this two steps:

Step1: Organize the directory as: your_dataset_path/subject_ids/walking_conditions/views. E.g. CASIA-B/001/nm-01/000/.
Step2: Cut and align the raw silhouettes with pretreatment.py. (See pretreatment for details.) Welcome to try different ways of pretreatment but note that the silhouettes after pretreatment MUST have a size of 64x64.

Futhermore, you also can test our code on OU-MVLP Dataset. The number of channels and the training batchsize is slightly different for this dataset. For more detail, please refer to our paper.

Pretreatment

pretreatment.py uses the alignment method in this paper. Pretreatment your dataset by

python pretreatment.py --input_path='root_path_of_raw_dataset' --output_path='root_path_for_output'

--input_path (NECESSARY) Root path of raw dataset.
--output_path (NECESSARY) Root path for output.
--log_file Log file path. #Default: './pretreatment.log'
--log If set as True, all logs will be saved. Otherwise, only warnings and errors will be saved. #Default: False
--worker_num How many subprocesses to use for data pretreatment. Default: 1

Configuration

In config.py, you might want to change the following settings:

dataset_path (NECESSARY) root path of the dataset (for the above example, it is "gaitdata")
WORK_PATH path to save/load checkpoints
CUDA_VISIBLE_DEVICES indices of GPUs

Train

Train a model by

python train.py

--cache if set as TRUE all the training data will be loaded at once before the training start. This will accelerate the training. Note that if this arg is set as FALSE, samples will NOT be kept in the memory even they have been used in the former iterations. #Default: TRUE

Evaluation

Evaluate the trained model by

python test.py

--iter iteration of the checkpoint to load. #Default: 80000
--batch_size batch size of the parallel test. #Default: 1
--cache if set as TRUE all the test data will be loaded at once before the transforming start. This might accelerate the testing. #Default: FALSE

It will output Rank@1 of all three walking conditions. Note that the test is parallelizable. To conduct a faster evaluation, you could use --batch_size to change the batch size for test.

OUMVLP

Since the huge differences between OUMVLP and CASIA-B, the network setting on OUMVLP is slightly different.

The alternated network's code can be found at ./work/OUMVLP_network. Use them to replace the corresponding files in ./model/network.
The checkpoint can be found here.
In ./config.py, modify 'batch_size': (8, 16) into 'batch_size': (32,16).
Prepare your OUMVLP dataset according to the instructions in Dataset & Preparation.

To Do List

Transformation: The script for transforming a set of silhouettes into a discriminative representation.

Authors & Contributors

GaitSet is authored by Hanqing Chao, Yiwei He, Junping Zhang and JianFeng Feng from Fudan Universiy. Junping Zhang is the corresponding author. The code is developed by Hanqing Chao and Yiwei He. Currently, it is being maintained by Hanqing Chao and Kun Wang.

Citation

Please cite these papers in your publications if it helps your research:

@ARTICLE{chao2019gaitset,
  author={Chao, Hanqing and Wang, Kun and He, Yiwei and Zhang, Junping and Feng, Jianfeng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set}, 
  year={2021},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3057879}}

Link to paper:

GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set

License

GaitSet is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, contact Junping Zhang.

AbnerHqC / GaitSet

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