  Crowd counting from a single image is a challenging task due to high
appearance similarity, perspective changes and severe congestion. Many methods
only focus on the local appearance features and they cannot handle the
aforementioned challenges. In order to tackle them, we propose a Perspective
Crowd Counting Network (PCC Net), which consists of three parts: 1) Density Map
Estimation (DME) focuses on learning very local features for density map
estimation; 2) Random High-level Density Classification (R-HDC) extracts global
features to predict the coarse density labels of random patches in images; 3)
Fore-/Background Segmentation (FBS) encodes mid-level features to segments the
foreground and background. Besides, the DULR module is embedded in PCC Net to
encode the perspective changes on four directions (Down, Up, Left and Right).
The proposed PCC Net is verified on five mainstream datasets, which achieves
the state-of-the-art performance on the one and attains the competitive results
on the other four datasets. The source code is available at
https://github.com/gjy3035/PCC-Net.
