Learning Transformation Maps for Crowd Analysis
摘要
Two important tasks in crowd analysis are crowd counting and crowd localization. In this paper, we introduce map-based crowd counting and localization methods, including density map-based methods, dot mask map-based methods, and distance transformation map-based methods. In addition, we combine the map-based methods with different losses. Finally, we compare the counting and localization performance of map-based crowd counting and localization methods on two benchmark datasets to evaluate the effectiveness of existing maps and loss functions.