Problem
Research questions and friction points this paper is trying to address.
eye-tracking
urban safety perception
visual cues
gaze dynamics
scene semantics
UrbanGazeVis: A Visualization System for Analyzing Eye-Tracking Data on Urban Safety Perception
🤖 AI Summary
研究通过使用头戴式眼动追踪技术收集数据，并开发了UrbanGazeVis系统来分析这些数据，以理解人们如何根据视觉线索判断城市街道的安全性。
📝 Abstract
Perceived safety in streetscapes depends on where people look, yet how gaze relates to visual cues of urban disorder remains poorly understood. Prior work treats safety as an image-level label, offering little insight into how attention to specific elements (e.g, buildings, greenery, people, signs of decay) shapes these judgments. We present a head-mounted eye-tracking study in which 30 participants viewed and rated the safety of 150 street-view images from Rio de Janeiro using a HoloLens 2 headset. Gaze traces were mapped onto semantic segments and disorder cues (e.g., damaged walls, graffiti, overhead cables), yielding a multimodal dataset linking gaze dynamics, scene semantics, and safety scores. To analyze it, we introduce UrbanGazeVis, an interactive visual analytics system with image- and participant-centric views that connects the spatial, temporal, and semantic dimensions of gaze to perceived safety, supporting comparisons between safe and unsafe scenes, inspection of divergent ratings for similar images, and region-of-interest analysis via glyph-based summaries. Statistical models show that sustained attention to physical disorder is associated with lower perceived safety, while the visual analysis reveals context-specific effects often masked by global aggregation. Together, these analyses offer actionable insights for urban design and planning.
Innovation
Methods, ideas, or system contributions that make the work stand out.
UrbanGazeVis
eye-tracking data
urban safety perception
multimodal dataset
visual analytics
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