I. Introduction  
A. Related Works and Scope  
B. Related Previous Reviews and Surveys  
C. Contributions of This Paper  

II. Taxonomy for Crowd Counting  
A. Representative Network Architectures for Crowd Counting  
B. Learning Paradigm  
C. Inference Manner  
D. Supervision Form  
E. Domain Adaptation  
F. Instance-/Image-based Supervision  

III. Datasets  

IV. Evaluation Metrics  

V. Benchmarking and Analysis  

VI. Discussion  
A. Dataset Construction  
B. The Quality of Density Maps  
C. Domain Adaptation or Transfer Learning  
D. Robustness for Background  
E. Universality or Generalization  
F. Lightweight Network  
G. Combination of Image and Video  
H. Wider-view Crowd Counting  
I. Localization, Classification, and Tracking Beyond Object Counting  
J. Small or Tiny Object Counting  

VII. Conclusion