A-CCNN: adaptive ccnn for density estimation and crowd counting
This work addresses crowd counting for public safety and urban planning, but it appears incremental as it builds on existing CNN methods with adaptive scaling.
The paper tackles the problem of crowd counting with vision-based techniques by proposing an Adaptive Counting Convolutional Neural Network (A-CCNN) to handle scale variation and occlusion, resulting in improved accuracy that outperforms state-of-the-art methods on benchmark datasets.
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.