PaDNet: Pan-Density Crowd Counting

Crowd counting in varying density scenes is a challenging problem in artificial intelligence (AI) and pattern recognition. Recently, deep convolutional neural networks (CNNs) are used to tackle this problem. However, the single-column CNN cannot achieve high accuracy and robustness in diverse density scenes. Meanwhile, multi-column CNNs lack effective way to accurately learn the features of different scales for estimating crowd density. To address these issues, we propose a novel pan-density level deep learning model, named as Pan-Density Network (PaDNet). Specifically, the PaDNet learns multi-scale features by three steps. First, several sub-networks are pre-trained on crowd images with different density-levels. Then, a Scale Reinforcement Net (SRN) is utilized to reinforce the scale features. Finally, a Fusion Net fuses all of the scale features to generate the final density map. Experiments on four crowd counting benchmark datasets, the ShanghaiTech, the UCF\_CC\_50, the UCSD, and the UCF-QRNF, indicate that the PaDNet achieves the best performance and has high robustness in pan-density crowd counting compared with other state-of-the-art algorithms.