ViWise:融合视觉和无线传感数据用于轨迹关系识别

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Fang-Jing Wu, Sheng-Wun Lai, Sok-Ian Sou
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引用次数: 0

摘要

人们通常会形成一种社会结构(例如,领导者-追随者,同伴或独立团体),以便更好地相互作用,从而对移动时遇到的可见场景和不可见无线信号具有相似的感知。许多移动驱动的应用程序都非常重视识别人与人之间的轨迹关系。这项工作为视觉和无线数据建模,以量化一对用户之间的轨迹相似性。我们设计了一个视觉和无线传感器融合系统,称为ViWise,它结合了由可穿戴视觉设备收集的第一人称视频帧和由个人移动设备广播的无线数据包,用于识别移动群体中更细粒度的轨迹关系。当人们走相似的轨迹时,他们通常会分享相似的视觉场景。它们的无线数据包被周围的无线基站(在这项工作中称为无线扫描器)观察到,通常包含类似的模式。我们从两个角度对用户看到的物理对象的视觉特征进行建模:具有像素特征的微观尺度图像结构和宏观尺度语义上下文。另一方面,我们根据用户轨迹上遇到的无线扫描器对无线数据包的特征进行建模。给定两个用户的轨迹,将其可见视频帧和不可见无线数据包背后的轨迹特征融合在一起,以计算视觉-无线数据相似度,从而量化他们所采取的轨迹之间的相关性。我们利用建模的视觉无线数据相似性来识别用户轨迹中的社会结构。室内和室外环境的综合实验结果表明,所提出的ViWise在轨迹关系识别方面具有很强的鲁棒性,准确率在90%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ViWise: Fusing Visual and Wireless Sensing Data for Trajectory Relationship Recognition
People usually form a social structure (e.g., a leader-follower, companion, or independent group) for better interactions among them and thus share similar perceptions of visible scenes and invisible wireless signals encountered while moving. Many mobility-driven applications have paid much attention to recognizing trajectory relationships among people. This work models visual and wireless data to quantify the trajectory similarity between a pair of users. We design a visual and wireless sensor fusion system, called ViWise, which incorporates the first-person video frames collected by a wearable visual device and the wireless packets broadcast by a personal mobile device for recognizing finer-grained trajectory relationships within a mobility group. When people take similar trajectories, they usually share similar visual scenes. Their wireless packets observed by ambient wireless base stations (called wireless scanners in this work) usually contain similar patterns. We model the visual characteristics of physical objects seen by a user from two perspectives: micro-scale image structure with pixel-wise features and macro-scale semantic context. On the other hand, we model characteristics of wireless packets based on the encountered wireless scanners along the user’s trajectory. Given two users’ trajectories, their trajectory characteristics behind the visible video frames and invisible wireless packets are fused together to compute the visual-wireless data similarity that quantifies the correlation between trajectories taken by them. We exploit modeled visual-wireless data similarity to recognize the social structure within user trajectories. Comprehensive experimental results in indoor and outdoor environments show that the proposed ViWise is robust in trajectory relationship recognition with an accuracy of above 90%.
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来源期刊
CiteScore
5.20
自引率
3.70%
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