PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
This work addresses crowd counting for surveillance and public safety applications, but it is incremental as it builds on existing methods with specific architectural improvements.
The authors tackled the problem of crowd counting from single images by proposing PCC Net, which addresses challenges like perspective changes and congestion through a multi-part architecture including density map estimation and perspective encoding, achieving state-of-the-art performance on one dataset and competitive results on four others.
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.