CrossNet: Boosting Crowd Counting with Localization

Generating high-quality density maps is a crucial step in crowd counting. It is obvious that exploiting the head location of the people can naturally highlight the crowded area and eliminate the interference of background noise. However, existing crowd counting methods are still tricky to reasonably use location in density generation. In this paper, a novel location-guided framework named CrossNet is proposed for crowd counting, which integrates location supervision into density maps through dual-branch joint training. First, a new branching network is proposed to localize the potential positions of pedestrians. With the help of supervision induced from the localization branch, Location Enhancement (LE) module is designed to obtain high-quality density maps by positioning foreground regions. Second, Adaptive Density Awareness Attention (ADAA) module is engaged to enhance localization accuracy, which can efficiently use the density of the counting branch to adaptively capture the error-prone dense areas of the location maps. Finally, Density Awareness Localization (DAL) loss is offered to allocate attention to the crowd density levels, which delivers more focus on regions with high densities and less concentration on areas with low densities. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art approaches both in crowd counting and crowd localization.