Abstract
This paper presents a feasibility analysis of saliency detection algorithms for stereo video retargeting in assistive augmented reality (AR) systems. Targeting the Magic Leap 2 headset, we benchmark a diverse set of classical and lightweight deep learning models, including AIM, SUN, IKN, BMS, U 2 -Net, and SAM-Net, evaluating their performance in terms of segmentation accuracy, inference speed, and deployment efficiency. Unlike prior approaches that rely on computationally intensive deep neural networks, our goal is to identify models compatible with the limited hardware acceleration available in wearable AR devices. A personalized visual mask, calibrated through user-specific visual profiles, guides the adaptive relocation of salient regions into areas of preserved vision, enabling dynamic field-of-view compensation for individuals with peripheral vision impairments. Experimental results demonstrate that U 2 -Net Lite and SAM-Net offer a practical balance between accuracy and computational cost, while classical methods like BMS remain viable for real-time AR integration. This work contributes to the development of low-latency, vision-aware AR systems, advancing stereo-retargeting pipelines for accessibility-focused applications. All benchmarking code, evaluation scripts, and model wrappers are available upon request.