Density-Based Flow Mask Integration via Deformable Convolution for Video People Flux Estimation

Crowd counting is currently applied in many areas, such as transportation hubs and streets. However, most of the research still focuses on counting the number of people in a single image, and there is little research on solving the problem of calculating the number of non-repeated people in a video segment. Currently, multiple object tracking is mainly relied upon for video counting, but this method is not suitable for situations where the crowd density is too high. Therefore, we propose a Flow Mask Integration Deformable Convolution network (FMDC) combined with Inter-Frame Head Contrastive Learning (IFHC) to predict the situation of people entering and exiting the screen in a density-based manner. We verify that our proposed method is highly effective in densely populated situations and diverse scenes, and the experimental results show that our proposed method surpasses existing methods.