An approach to automatically recognizing the movement of people at a pedestrian crossing presented in the article. This approach includes two main procedures, for each of which program code commands are given in the C# programming language using the EMGU computer vision library. In the first procedure, pedestrian detection is carried out using a combination of directional gradient histogram and support vector methods. The second procedure allows you to read frames from a video sequence and process them. This approach allows detecting the movements of people at a pedestrian crossing without using specialized neural networks. At the same time, the method proposed in the article demonstrated sufficient reliability of human movement recognition, which indicates its applicability in real conditions.

In the modern world, the problems arising in the field of traffic are of great importance. In order to solve existing problems, various intelligent systems are being developed, one of which is the Smart City system. This work is devoted to the development of an information and analytical system (IAS) for controlling an intelligent traffic light. The presented system consists of two levels, each of which contains a set of specific operations. The first level is responsible for detecting objects, in particular pedestrians and cars at the intersection, and the second level calculates the operating time of traffic light signals for the control signal that is transmitted to the device. For comparative analysis, the combined method (HOG+SVM) Histogram of oriented gradients was chosen, based on counting the number of gradient directions on individual image areas and Support Vector Machines, which are used to construct hyperplanes in n-dimensional space in order to separate objects belonging to different classes. The results of an experimental study, during which the recognition of objects in images was carried out, showed the superiority of the developed information and analytical system over existing methods. The average accuracy of detecting pedestrians and cars through the IAS was 69.4%. In addition, according to the experiment, it was concluded that the accuracy of detecting objects in images is directly proportional to the distance from the video camera to the object.