Person Search GCN Project

This project implements Context Graph of paper Learning Context Graph for Person Search (CVPR 2019 Oral).

This repository shows how we can use graph convolution model to employ context information and improve person search performance.

Environment:

python(3.6), pytorch(0.4.1), numpy(1.14.1), matplotlib(3.0.2), tqdm, pickle

Preparation

1. Clone this repo

git clone https://github.com/sjtuzq/person_search_gcn.git

2. modify data_path, log_path and neighbor_num in config.py

3. download dataset here, then put it into data_path folder. The features are generated by the method in Joint Detection and Identification Feature Learning for Person Search, whose code can be found here.

4. prepare dataset generate the raw data feature into the paired form: persons in the same images are grouped together.

python prepare.py

Experiments

1. train and test the gcn model

train graph convolution model: with the pair selected by distance.

python train_gcn1.py

The top-1 acc can be imporved from 78.6% to 81.6%, mAP from 75.4% to 80.2%.