Development and Testing of Algorithms for Vehicle Type Recognition and Car Tracking with Photo and Video Traffic Enforcement Cameras

The work is devoted to the research that was carried out within the framework of computer vision problems applicable to the analysis of images and video information with vehicles. We solve the problem of classifying vehicles. We analyze the drawbacks of Haar features and convolutional neural networks and test the obtained networks using the key point method; we construct an integral algorithm that includes several networks, and we further validate it on a large number of real photographs and types of vehicles. Next, we solve the task to develop a software framework for tracking vehicles by analyzing adjacent photographs from a video sequence. After that, we consider the tracking task in more detail. We analyze modern tracking algorithms using machine learning and describe our implemented tracker with support for the appearance of obstacles between the camera and a moving vehicle. As a result, we propose algorithms and open-source software that, after being configured for specific cameras, can be used in traffic analysis systems.