Crowd counting has become necessary in the age of information technology and network development; with many different goals, people have needed more services to meet their work. Therefore, crowd counting is also necessary for daily life, such as solid classrooms, vigorous meetings, conferences, etc. Based on practical needs, we propose counting the number of people in an image, known as crowd counting. Using several software techniques, we found that DM-Count outperformed previous advanced methods on two large-scale counting datasets by a wide margin in Mean Absolute Error. Furthermore, the VGG16 model for the algorithm does not certainly use the current

VGG19 and still shows good higher performance. We analyze multiple people marked with dots in each training image. Existing crowd-counting methods must either smooth each annotated drop with a Gaussian distribution or estimate the likelihood of each pixel given the annotated point. This paper proposes a crowd-counting model using deep learning and Computer Vision to improve performance by handling perspective distortion by intelligently using many features generated during coding. The use of Deep Learning and Computer Vision experimentally shows that our method can improve up to 34.91% Mean Absolute Error and 26.25% of Root Mean Squared Error compared to the referenced methods.