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Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays

机译:使用射频标签阵列挖掘频繁轨迹模式以进行活动监控

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摘要

Activity monitoring, a crucial task in many applications, is often conducted expensively using video cameras. Effectively monitoring a large field by analyzing images from multiple cameras remains a challenging issue. Other approaches generally require the tracking objects to attach special devices, which are infeasible in many scenarios. To address the issue, we propose to use RF tag arrays for activity monitoring, where data mining techniques play a critical role. The RFID technology provides an economically attractive solution due to the low cost of RF tags and readers. Another novelty of this design is that the tracking objects do not need to be equipped with any RF transmitters or receivers. By developing a practical fault-tolerant method, we offset the noise of RF tag data and mine frequent trajectory patterns as models of regular activities. Our empirical study using real RFID systems and data sets verifies the feasibility and the effectiveness of this design.
机译:活动监视是许多应用程序中的一项至关重要的任务,通常使用摄像头进行昂贵的操作。通过分析来自多个摄像机的图像来有效监视大视野仍然是一个具有挑战性的问题。其他方法通常要求跟踪对象连接特殊设备,这在许多情况下是不可行的。为了解决该问题,我们建议使用RF标签阵列进行活动监控,其中数据挖掘技术起着至关重要的作用。由于RF标签和读取器的低成本,RFID技术提供了一种经济上有吸引力的解决方案。该设计的另一个新颖之处在于,跟踪对象不需要配备任何RF发射器或接收器。通过开发一种实用的容错方法,我们抵消了RF标签数据的噪声并挖掘了频繁的轨迹模式作为常规活动的模型。我们使用真实的RFID系统和数据集进行的经验研究验证了该设计的可行性和有效性。

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