  We propose a multitask approach for crowd counting and person localization in
a unified framework. As the detection and localization tasks are
well-correlated and can be jointly tackled, our model benefits from a multitask
solution by learning multiscale representations of encoded crowd images, and
subsequently fusing them. In contrast to the relatively more popular
density-based methods, our model uses point supervision to allow for crowd
locations to be accurately identified. We test our model on two popular crowd
counting datasets, ShanghaiTech A and B, and demonstrate that our method
achieves strong results on both counting and localization tasks, with MSE
measures of 110.7 and 15.0 for crowd counting and AP measures of 0.71 and 0.75
for localization, on ShanghaiTech A and B respectively. Our detailed ablation
experiments show the impact of our multiscale approach as well as the
effectiveness of the fusion module embedded in our network. Our code is
available at: https://github.com/RCVLab-AiimLab/crowd_counting.
