1 INTRODUCTION

2 PROBLEM FORMULATION AND CHALLENGES

3 BENCHMARKS
3.1 Datasets
3.2 Evaluation Criteria

4 REGULAR PIPELINE FOR PAR
4.1 Multi-task Learning
4.2 Multi-label Learning

5 DEEP NEURAL NETWORKS

6 THE REVIEW OF PAR ALGORITHMS
6.1 Global Image-based Models
6.2 Part-based Models
6.3 Attention-based Models
6.4 Sequential Prediction-based Models
6.5 Loss Function based Models
6.6 Curriculum Learning-based Algorithms
6.7 Graphic Model based Algorithms
6.8 Other Algorithms

7 APPLICATIONS

8 FUTURE RESEARCH DIRECTIONS
8.1 More Accurate and Efficient Part Localization Algorithm
8.2 Deep Generative Models for Data Augmentation
8.3 Further Explore the Visual Attention Mechanism
8.4 New Designed Loss Functions
8.5 Explore More Advanced Network Architecture
8.6 Prior Knowledge guided Learning
8.7 Multi-modal Pedestrian Attribute Recognition
8.8 Video-based Pedestrian Attribute Recognition
8.9 Joint Learning of Attribute and Other Tasks

9 CONCLUSION