A-CCNN: adaptive ccnn for density estimation and crowd counting

Abstract

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.

Single Convolutional Neural Network With Three Layers Model for Crowd Density Estimation

“…Kasmani et al [32], proposed an Adaptive Counting Convolutional Neural Network (A-CCNN) that uses an ideally trained CCNN model. This model is used to analyze each component of an input image in order to properly estimate the appropriate density map.…”