MSPNET: Multi-Supervised Parallel Network for Crowd Counting

Abstract

Crowd counting has a wide range of applications such as video surveillance and public safety. Many existing methods only focus on improving the accuracy of counting but ignore the importance of density maps. It's no doubt that a high-quality density map contains more information such as localization and movement of the crowd. In this paper, we propose a multi-supervised parallel network (MSPNet) to achieve high accuracy of crowd counting and generate high-quality density maps. We conduct multiple supervisions in the training process, which can supplement the details lost in pooling and up-sampling operations to improve the quality of density maps. In addition, to reduce the impact of background noise, the attention mechanism is employed to help the network focus on the crowd. Extensive experiments on two mainstream benchmarks show that MSPNet achieves significantly improvement over the state-of-the-art in terms of counting accuracy and the quality of density maps.