A Single-Column Convolutional Neural Networks for Crowd Counting
Phuc Thinh Do, Manh Thuong Phan, Thien Tam Chan Le · 2019 6th NAFOSTED Conference on Information and Computer Science (NICS) · 2019
Crowd counting is an important task in the surveillance camera system. The methods of crowd counting have changed from counting each object in the scene to estimating the density map and the count will be reached by integrating the entire density map. The previous methods used multi-column convolutional neural networks (CNN) with different sizes of filters to describe objects of different scales. To increase the performance of the multi-column model, some methods suggest adding a classifier to select a single-column CNN that gives the expecting results. However, having multiple columns and filters will complicate the model and trained model with limited crowd data will estimate bad density maps. To solve this problem, we propose the "Single-Column CNN" (SCC) method which uses a single-column CNN with only one size of filters (3x3) and replaces normal convolution with different sizes of filters by the dilated convolution. Our approach makes the density map estimation model become a simple but strong CNN. Experiments show that our method achieves significant improvements compared to the method using multi-column CNN in the ShanghaiTech dataset.