An Overview of Crowd Counting on Traditional and CNN-based Approaches

Abstract

Crowd counting, which is one of the primary research lines of computer vision, has achieved significant advancement due to the affordable computational cost, and it is a particularly useful means of ensuring public security in public places, especially in crowded places. In recent years, crowd counting methods based on public places have emerged one after another given crowd congestion. Firstly, this paper introduces in detail from traditional approaches to deep learning approaches, focusing on crowd counting approaches based on the convolution neural network (CNN), and compares and analyzes the advantages and disadvantages of each approach. Next, this paper summarizes the commonly used datasets and the main indicators of evaluating crowd counting algorithms. Finally, this paper expounds on the challenges existing in the field of crowd counting and the possible research directions in the future.