Algorithms for monitoring moving objects are of great interest in the field of computer vision. The paper presents the results of the development and research of image analysis algorithms for monitoring vehicles in a video stream. Currently, the monitoring of vehicles, especially in production areas, is performed manually by operators. Developing practical applications of video analytics will reduce operator workload, increase speed and accuracy of decision making, and automate the process of collecting and generating reporting statistics on plant transportation. Research was conducted and a video analysis system was developed that includes detection algorithms, classifications by vehicle type, color, and model, and vehicle tracking in the video stream. It should be noted that the architecture of existing solutions, as a rule, is monolithic and does not allow to refine and embed new unique modules to solve emerging problems. In modern conditions, in order to cover new requirements in the field of object recognition and detection in large vehicle flows, it is necessary to develop and implement new solutions. The results of the work of the algorithms on real images of existing datasets and the developed own dataset are presented. In this case, the objects are under different lighting conditions, in different angles, are rebuilt in the stream, there is an overlap of objects. The developed system has a microservice architecture, which allows to adapt the solutions to specific tasks.