Cascade-guided multi-scale attention network for crowd counting

The performance of crowd counting based on density estimation has been greatly improved with the development of deep learning. However, it is still a major issue to obtain high-quality density map due to the clutter of background, as well as the interference of perspective changes within and between scenes. In this paper, we propose a cascade-guided crowd counting network, which is mainly embedded with scale aware model (SAM) and attention aware model (AAM). First, SAM considers share-net design and multi-directional perspective transform in convolution to deal with multi-scale varying and smooth transition, while reducing the background noise in shallow features. Second, AAM further encodes the semantic inter dependencies by using the two-dimensional features of location and channel in order to let the network learn to pay attention to the key information. Finally, the global and local features are concatenated and taken into decoder to generate the estimated density map for crowd counting. Comprehensive experiments based on three established datasets show that the proposed method not only has higher accuracy, but also has stronger robustness to scale variation and background noise.