Population growth and expansion of urban areas increase the need for the introduction of intelligent passenger traffic monitoring systems. Accurate estimation of the number of passengers is an important condition for improving the efficiency, safety and quality of transport services. This paper proposes an approach to the automatic detection and counting of people using computer vision and deep learning methods. While YOLOv8 and DeepSORT have been widely explored individually, our contribution lies in a task-specific modification of the DeepSORT tracking pipeline, optimized for dense passenger environments, strong occlusions, and dynamic lighting, as well as in a unified architecture that integrates detection, tracking, and automatic event-log generation. Our new proprietary dataset of 4047 images and 8918 labeled objects has achieved 92% detection accuracy and 85% counting accuracy, which confirms the effectiveness of the solution. Compared to Mask R-CNN and DETR, the YOLOv8 model demonstrates an optimal balance between speed, accuracy, and computational efficiency. The results confirm that computer vision can become an efficient and scalable replacement for traditional sensory passenger counting systems. The developed architecture (YOLO + Tracking) combines recognition, tracking and counting of people into a single system that automatically generates annotated video streams and event logs. In the future, it is planned to expand the dataset, introduce support for multicamera integration, and adapt the model for embedded devices to improve the accuracy and energy efficiency of the solution in real-world conditions.

The article is devoted to the program "Converter", which allows you to translate the geologic-lithological profile of a mineral field into a digital format in the form of a two-dimensional array. The object-oriented programming language Python was used to write the program. The NumPy, OpenCV, and MatPlotlib libraries are actively used. The implementation of this program is based on image segmentation and finding the prevailing colors in the OpenCV library. Image segmentation is a preliminary step in image processing. The obtained values allow you to find out the density distribution in the area under consideration. The program "Converter" has a good graphical representation of the results obtained using the MatPlotlib library. The program writes the final converted result as a two-dimensional array to a text file along the desired path. Thus, the matrix is easy to read for further use in conjunction with other programs. The purpose of this work was to create a program that converts the geologic-lithological profile of the field into a digital format in the form of a two-dimensional array, for further use of this matrix as the distribution density of the oil field. The "Converter" program converts any geologic-lithological profile into a two-dimensional array in a matter of minutes.