The task involves refining dynamic object tracking within SLAM for accurate VR map updates in a remote environment. Evaluating diverse algorithms for real-time object detection, a hybrid approach combining deep learning with traditional computer vision methods emerges as a promising solution, leveraging their respective strengths. Model selection involves balancing factors like computational resources, accuracy, and real-time performance, enriching the toolkit for addressing semantic segmentation challenges in computer vision.
A detailed comparative analysis highlights various model strengths but refrains from favoring a specific one, as each excels in different scenarios. This thorough exploration aims to enhance understanding and decision-making in selecting suitable models for dynamic object segmentation in telerobotic applications.