Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts have been reported, real-world problems, such as huge variation in subjects' sizes in images and serious occlusion among people, make it still a challenging problem. In this paper, we propose an Adaptive Counting Convolutional Neural Network (A-CCNN) and consider the scale variation of objects in a frame adaptively so as to improve the accuracy of counting. Our method takes advantages of contextual information to provide more accurate and adaptive density maps and crowd counting in a scene. Extensively experimental evaluation is conducted using different benchmark datasets for object-counting and shows that the proposed approach is effective and outperforms state-of-the-art approaches.
@article{arxiv.1804.06958,
title = {A-CCNN: adaptive ccnn for density estimation and crowd counting},
author = {Saeed Amirgholipour Kasmani and Xiangjian He and Wenjing Jia and Dadong Wang and Michelle Zeibots},
journal= {arXiv preprint arXiv:1804.06958},
year = {2018}
}