Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

By Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Huang Da, Jun Cheng, Bin Hu. In IJCAI 2020.

Introduction

This is the official implementation of the self-supervised gait encoding model presented by "Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification". The codes are used to reproduce experimental results of the proposed Attention-basd Gait Encodings (AGEs) in the paper.

Requirements

Python 3.5
Tensorflow 1.10.0 (GPU)

Datasets

We provide three already preprocessed datasets (BIWI, IAS, KGBD) on
https://share.weiyun.com/5faKfq4 password： ma385h
Two already trained models (BIWI, IAS) are saved in this repository, and all three models can be acquired on
https://share.weiyun.com/5EBPkPZ password： 6xpj8r
Please download the preprocessed datasets Datasets/ and the model files Models/ into the current directory.

The original datasets can be downloaded from: http://robotics.dei.unipd.it/reid/index.php/downloads (BIWI and IAS-Lab)
https://www.researchgate.net/publication/275023745_Kinect_Gait_Biometry_Dataset_-_data_from_164_individuals_walking_in_front_of_a_X-Box_360_Kinect_Sensor (KGBD)

Usage

To (1) train the self-supervised gait encoding model to obtain AGEs and (2) validate the effectiveness of AGEs for person Re-ID on a specific dataset with a recognition network, simply run the following command:

# --attention: LA (default), BA  --dataset: BIWI, IAS, KGBD  --gpu 0 (default)
python train.py --dataset BIWI

Please see train.py for more details.

To print evaluation results (Rank-1 accuracy/nAUC) of person re-identification (Re-ID) on the testing set, run:

# --attention: LA (default), BA  --dataset: BIWI, IAS, KGBD  --gpu 0 (default)
python evaluate.py --dataset BIWI

Please see evaluate.py for more details.

Citation

@inproceedings{rao2020self,
	title="Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification",
	author="Haocong {Rao} and Siqi {Wang} and Xiping {Hu} and Mingkui {Tan} and Huang {Da} and Jun {Cheng} and Bin {Hu}",
	booktitle="IJCAI 2020: International Joint Conference on Artificial Intelligence",
	year="2020"
}

License

SGE-LA is released under the MIT License.

ssjsusie/SGE-LA