Counting crowds with varying densities via adaptive scenario discovery framework

The task of crowd counting is to estimate the number of pedestrian in crowd images. Due to camera perspective and physical barriers among dense crowds, how to construct a robust counting model for varying densities and various scenarios has become a challenging problem. In this paper, we propose an adaptive scenario discovery framework for counting crowds with varying densities. The framework is structured with two parallel pathways that are trained to represent different crowd densities and present in the proper geometric configuration using different sizes of the receptive field. A third adaption branch is designed to adaptively recalibrate the pathway-wise responses by discovering and modeling the dynamic scenarios implicitly. We conduct experiments using the adaptive scenario discovery framework on five challenging crowd counting datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.