IDENTIFICATION PARAMETERS OF DYNAMIC OBJECTS USING TRANSFORMER WITH OPTICAL FLOW AND ENSEMBLE METHODS

Abstract

The article presents an approach to identifying the parameters of dynamic objects in a video stream using a transformer-based architecture, the GeoNet model, and ensemble machine learning methods, namely bagging and boosting. The identification of parameters such as position, velocity, direction of movement, and depth is of significant importance for a wide range of applications, including autonomous driving, robotics, and video surveillance systems. The paper describes a comprehensive system that integrates the spatiotemporal characteristics of a video stream by computing optical flow and depth maps using GeoNet, further analyzing them through a transformer, and enhancing accuracy via ensemble methods. GeoNet, as a deep convolutional neural network, combines the tasks of depth estimation and optical flow within a single architecture, enabling accurate 3D scene reconstruction. The use of a transformer allows modeling global dependencies across video frames and improves the accuracy of object classification and detection. At the same time, bagging reduces variance by averaging the results of several models trained on different subsets, while boosting focuses on difficult examples to improve prediction accuracy. The proposed system achieves high accuracy under conditions of dynamic background, lighting changes, occlusions, and noise, making it adaptable for real-time use in complex scenes. A detailed description of each system component is provided: the GeoNet architecture, transformer modules, implementation of bagging and boosting, and the result fusion algorithm. The expected results are intended to demonstrate the effectiveness of integrating deep learning methods with classical ensemble approaches for high-precision dynamic object identification tasks. The proposed methodology opens new prospects for the development of next-generation intelligent computer vision systems.