<p>Object segmentation is a fundamental research area within the computer vision community. Video object segmentation in dynamic environments presents significant challenges due to the complexity and variability of scenes, including occlusions, lighting changes, and object deformations. To overcome these challenges, we propose a semi-supervised video object segmentation framework employing a novel one-shot learning approach based on fully convolutional neural networks (FCNs). Our method leverages deep learning techniques to perform efficient and accurate segmentation across diverse objects and scenes. The proposed one-shot object segmentation method (OSOSM) integrates broad semantic knowledge from pre-trained models with specific object characteristics from a single annotated frame to maintain temporal coherence and stability. By processing each frame independently yet coherently, the method ensures robustness against sudden scene changes and object motions. Additionally, our framework incorporates local contour cues and global optimization to enhance object contour delineation, which is crucial for precise segmentation. We conducted extensive experiments on benchmarks, such as DAVIS and YouTube-VOS, to evaluate the effectiveness of our approach. The results demonstrate that our OSOSM method achieves superior segmentation accuracy and robustness compared to existing techniques, particularly in handling dynamic and complex scenes. This study significantly advances the state-of-the-art in video object segmentation, providing a robust and efficient solution for applications in dynamic environments.</p>