Abstract
This paper introduces an innovative artificial intelligence (AI) method aimed at tackling the challenges associated with detecting and tracking moving objects in video surveillance systems. By utilizing self-organization through artificial neural networks, our approach effectively manages scenes with dynamic backgrounds and gradual changes in lighting, ensuring robust detection across different types of videos recorded by stationary cameras. In the realm of moving object detection, our method leverages the adaptability of neural networks, enabling precise detection in complex visual environments. For object tracking, we propose a combination of Kalman filtering techniques and a sophisticated matching model based on Multiple Hypothesis Testing, ensuring accurate and consistent tracking across frames. Through experimental validation using various color video sequences, we demonstrate the effectiveness and reliability of our approach, highlighting its potential to enhance the performance of surveillance systems in real-world scenarios.
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