Enhanced Video Frame Segmentation Using Modified Clustering Algorithm for Accurate Background and Foreground Segmentation

Abstract

Video frame segmentation is a critical task in computer vision with applications ranging from video surveillance to medical imaging. This work proposes a Modified Clustering Algorithm designed to enhance accuracy and adaptability in video frame segmentation. The algorithm incorporates adaptive clustering mechanisms and temporal consistency to address challenges posed by dynamic backgrounds, lighting variations, and intricate object interactions. The algorithm initiates by initializing cluster centers based on the features extracted from the first frame of the video sequence. Subsequently, an adaptive clustering mechanism dynamically adjusts cluster centers, ensuring responsiveness to changing scenes. Temporal consistency is integrated by evaluating coherence across consecutive frames, enhancing segmentation accuracy in scenarios involving object motion and occlusions. Through an iterative process across frames, the algorithm generates segmentation outputs that combine spatial features and temporal information. Evaluation metrics such as Precision, Recall, and F1 Score demonstrate the algorithm's superior performance compared to traditional methods and existing state-of-the-art approaches. The algorithm's adaptability is showcased in its successful application across diverse domains, including video surveillance, medical imaging, and autonomous systems. However, challenges such as computational efficiency and generalization across diverse datasets persist, suggesting avenues for future research and optimization. The algorithm's versatility and precision underscore its potential in real-world applications requiring advanced video frame segmentation. This work contributes to the evolving field of computer vision by presenting a robust algorithm capable of handling the complexities inherent in dynamic video scenes. As video processing technology advances, the Modified Clustering Algorithm stands as a promising solution, offering improved segmentation accuracy and adaptability for diverse applications.