Abstract
This article presents the opinions of domestic and foreign scientists on methods for identifying important areas in video images. Applications like autonomous systems, medical imaging, surveillance, and object detection depend on the ability to recognise key regions in video pictures. To do this, a number of strategies have been devised, ranging from sophisticated deep learning models to conventional image processing methods. Traditional methods emphasise regions of interest using motion analysis, edge detection, and saliency mapping. By identifying spatial and temporal patterns, machine learning techniques—in particular, transformer-based topologies and convolutional neural networks (CNNs)—improve accuracy. Furthermore, robustness is increased via hybrid approaches that combine AI-driven models with manually created characteristics. This essay examines important approaches, their benefits, and drawbacks, providing guidance on how to choose the best methods for various uses.