MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture
This work addresses the problem of automated personal memory capture for users in daily scenarios, though it appears incremental as it builds on existing attention and analysis methods.
The authors tackled the challenge of personalized moment auto-capture by developing MemX, an attention-aware eyewear system that unifies human visual attention tracking and salient content analysis. They showed that MemX significantly improves attention tracking accuracy over eye-tracking-alone methods, with experimental results on the YouTube-VIS dataset and 30 participants, while maintaining high energy efficiency.
This work presents MemX: a biologically-inspired attention-aware eyewear system developed with the goal of pursuing the long-awaited vision of a personalized visual Memex. MemX captures human visual attention on the fly, analyzes the salient visual content, and records moments of personal interest in the form of compact video snippets. Accurate attentive scene detection and analysis on resource-constrained platforms is challenging because these tasks are computation and energy intensive. We propose a new temporal visual attention network that unifies human visual attention tracking and salient visual content analysis. Attention tracking focuses computation-intensive video analysis on salient regions, while video analysis makes human attention detection and tracking more accurate. Using the YouTube-VIS dataset and 30 participants, we experimentally show that MemX significantly improves the attention tracking accuracy over the eye-tracking-alone method, while maintaining high system energy efficiency. We have also conducted 11 in-field pilot studies across a range of daily usage scenarios, which demonstrate the feasibility and potential benefits of MemX.