Prerequisite: Caffe, Python 2.7 and Matlab.

1. Install Caffe, Python 2.7 and Maltab.

2. Download and prepare the dataset as follow:

   RAP Links

   ./data/RAP_annotation/RAP_annotation.mat
   ./data/RAP_dataset/*.png
   

   if you want to use body parts for attribute recognition, please exec this command to generate body part images.

   cd data
   matlab -nodisplay -r 'rap2_part_extraction'
   

3. Download the imagenet pretrained models. ImageNet pretrained models which is used in finetuning Baiduyun or GoogleDrive.

1. SVM-based models

   a. feature extraction, including ELF and pretrained CNN features:

   cd features/ELF-v2.0-Descriptor
   matlab -nodisplay -r 'Feature_Extraction_elf'
   matlab -nodisplay -r 'Feature_PCA_elf'
   

   download pretrained CNN models and run the follow commands to extract cnn features.

   cd features/CNN-v1.0-Descriptor
   matlab -nodisplay -r 'imagenet_feature_extraction_caffenet_single'
   matlab -nodisplay -r 'imagenet_feature_extraction_resnet_single'
   matlab -nodisplay -r 'imagenet_feature_extraction_caffenet_parts' [optional]
   matlab -nodisplay -r 'imagenet_feature_extraction_resnet_parts' [optional]
   
   

   compile the liblinear

   cd person-attribute/utils/liblinear-master/matlab
   matlab -nodisplay -r 'make'
   

   b. use svm to train attribute classifiers training: mixture clean and occlusion data, test: mixture clean and occlusion data.

   cd person-attribute/baseline-svm
   matlab -nodisplay -r 'v1_fullbody_analysis_mm' [one types of features]
   matlab -nodisplay -r 'v1_fullbody_analysis_mm_test' [all types of features]
   matlab -nodisplay -r 'v1_fullbody_analysis_mm_statistic' [summary the results]
   

   c. analysis of viewpoint training: maxture clean and occlusion data, test: mixture clean and occlusion data.

   cd person-attribute/baseline-svm
   matlab -nodisplay -r 'v2_fullbody_analysis_mm_viewpoint'
   matlab -nodisplay -r 'v2_fullbody_analysis_mm_viewpoint_statistic'
   
   

   training: clean, test: clean

   cd person-attribute/baseline-svm
   matlab -nodisplay -r 'v2_fullbody_analysis_cc'
   matlab -nodisplay -r 'v2_fullbody_analysis_cc_viewpoint'
   
   

   d. analysis of occlusion occlusion positions and types: training: clean, test: occlusion

   cd person-attribute/baseline-svm
   matlab -nodisplay -r 'v3_fullbody_analysis_co_test'
   matlab -nodisplay -r 'v3_fullbody_analysis_co_test_personvspersons'
   
   

   e. analysis of body parts

   cd person-attribute/baseline-svm
   matlab -nodisplay -r 'v4_parts_analysis_cc'
   matlab -nodisplay -r 'v4_parts_analysis_cc_test'
   

2. CNN-based models

   a. prepare the data splits.

   cd person-attribute/static
   matlab -nodisplay -r 'prepare_data'
   matlab -nodisplay -r 'prepare_data_parts'
   matlab -nodisplay -r 'prepare_data_binary'
   

   b. train the deep attribute classifiers with deepmar based on CaffeNet. For deepmar, acn, and their single attribute versions, the operators are in similar format.

   cd person-attribute/baseline-deepmar
   sh train_caffenet.sh
   sh test_caffenet.sh
   

The product of multiple attributes' prediction probability are used for person retrieval.

1. generate the attributes for attribute-based person retrieval.

   cd person-attribute/baseline-search
   matlab -nodisplay -r 'generate_multiquery_index'
   matlab -nodisplay -r 'generate_query_names'
   python generate_query_names.py
   

2. generate the attribute score based on the trained models

   cd person-attribute/baseline-search
   matlab -nodisplay -r 'generate_svm_score'
   python generate_cnn_score.py
   python generate_cnn_score_binary.py
   

3. evaluate the attribute-based person retrieval

   cd person-attribute/baseline-search
   matlab -nodisplay -r 'evaluate_multiquery_attributes'
   

1. hand-crafted features/pretrained cnn features with L2/XQDA/KISSME

   a. feature extraction, incluidng ELF, LOMO, GOG (window), JSTL

   cd features/ReID_GOG_v1.01
   matlab -nodisplay -r 'Feature_Extraction_gog'
   cd features/CNN-v1.0-Descriptor
   matlab -nodisplay -r 'jstl_feature_extraction_single'
   cd features/LOMO_XQDA/code
   matlab -nodisplay -r 'Feature_Extraction_lomo'
   

   b. feature evaluation

   cd person-reid/evaluation
   matlab -nodisplay -r 'rap2_evaluation_features'
   

2. end-to-end feature learning

   a. generate the data split file for training.

   cd person-reid/static
   matlab -nodisplay -r 'generate_att_trainval_test'
   matlab -nodisplay -r 'generate_ide_trainval_test'
   matlab -nodisplay -r 'generate_ide_att_trainval_test'
   matlab -nodisplay -r 'generate_ide_att_trainvaltest' 
   

   b. training with only ID classification loss, such as CaffeNet, ResNet50/ResNet101/ResNet152, DenseNet121, MSCAN.

   cd person-reid/baseline-IDE
   sh train_caffenet.sh
   

   c. training with only attribute classification loss

   cd person-reid/baseline-att
   sh train_caffenet.sh
   sh test_caffenet.sh [optional for attribute classification]
   

   d. training with attribute and ID classification losses

   cd person-reid/baseline-IDE-att
   sh train_caffenet.sh
   sh test_caffenet.sh [optional for attribute classification]
   

   e. deep feature extraction and evaluation.

   cd person-reid/evaluation
   sh rap2_feature_extraction_resnet.sh [one model per time]
   matlab -nodisplay -r 'rap2_test' [one model per time]
   

3. cross-day person retrieval

   a. person retrieval in the same day as query or the different day as query.

   cd person-reid/evaluation
   matlab -nodisplay -r 'rap2_test_control_single_cross'
   

   b. person retrieval from different day as query. The appearance would be partially different for the same person across different days.

   cd person-reid/evaluation
   matlab -nodisplay -r 'rap2_test_control_single_cross_quantively'
   

4. identity-level attribute vs. instance-level attributes for person re-identification.

   a. generate identity-level attributes from instance-level attributes for training.

   cd person-reid/static
   matlab -nodisplay -r 'generate_ide_att_trainval_test_control'
   

   b. train: instance-level attributes. The default setup.

   cd person-reid/baseline-IDE-att
   sh train_caffenet.sh
   

   c. train: identity-level attributes.

   cd person-reid/baseline-IDE-att-control
   sh train_caffenet.sh
   

   d. feature extraction and evaluation.

   cd person-reid/evaluation
   sh rap2_reid_extraction_resnet_control_identity_instance.sh 
   matlab -nodisplay -r 'rap2_test_control_identity_identity'
   matlab -nodisplay -r 'rap2_test_control_identity_instance'
   matlab -nodisplay -r 'rap2_test_control_instance_identity'
   

@article{li2018richly,
    title={A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios},
    author={Li, Dangwei and Zhang, Zhang and Chen, Xiaotang and Huang, Kaiqi},
    journal={IEEE Transactions on Image Processing},
    volume={28},
    number={4},
    pages={1575--1590},
    year={2019},
    publisher={IEEE}
}