2023 Volume 79 Issue 22 Article ID: 22-22025
It is important to count the number of people in a crowd during an event and commuting hours to prevent accidents. In recent years, a method has been developed to count the number of people easily from images by improving the speed and accuracy of deep learning. However, since crowd shooting conditions such as the installation angle and height of a camera are varied, depending on the size of a person and the degree of occlusion in a moving image, also how the person seems is varied. Thus, it is difficult to accurately count the number of people in a crowd under various conditions using one counting method. Consequently, it was considered that a counting method could be established to secure a certain degree of accuracy by categorizing scenes and conditions for shooting a crowd and changing a method to count the number of people best for the status of the crowd in each scene and shooting condition as necessary. In this research, four types of methods to count the number of people were applied to each scene to clarify the best method for each scene and problems toward the changing of an applied method.
