Vehicle recognition by silhouettes - a three-stage machine learning method in computer vision systems
In the areas of seaports, on sea and land routes, the task of accounting and control of various vehicles is urgent. To solve this problem, technical recognition systems of such means using special sensors are increasingly used. However, video images are not always of high quality for a number of reasons. Therefore, the theoretical and practical interest is the problem of recognition of vehicles on their heavily coarsened images - silhouettes. Our study uses experimental material from the UCI (UCI Machine Learning Repository) data repository and proposes a three-stage machine learning method to solve this problem. In the first cascade, a set of class precedents is formed - objects with their own local context-dependent metrics attached to them, providing the precedents with the maximum possible “scope of action”. At the second cascade, methods of searching for logical rules (corrective logical rules) are applied, for which the descriptions (attributes) of objects are the distances from the objects defined in the first cascade in their own local spaces. In the third cascade, the interaction of corrective logical rules is organized by their ensemble. The probability of correctly classifying vehicles was P = 0.972 (a 10-fold cross-validation method was used for the assessment). This result surpassed previously known results.