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

Activity monitoring, a crucial task in many applications, is often conducted expensively using video cameras. Also, effectively monitoring a large field by analyzing images from multiple cameras remains a challenging problem. In this paper, we introduce a novel application of the recently developed RFID technology: using 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 attach any transmitters or receivers, such as tags or readers. 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 our design.