- Online Risk Prediction for Indoor Moving Objects
- Aalborg Øst
Title: Online Risk Prediction for Indoor Moving Objects
Abstract: Technologies such as RFID and Bluetooth have received considerable attention for tracking indoor moving objects. In a time-critical indoor tracking scenario such as airport baggage handling, a bag has to move through a sequence of locations until it is loaded into the aircraft. Inefficiency or inaccuracy at any step can make the bag risky, i.e., the bag may be delayed at the airport or sent to a wrong airport. In this paper, we propose a novel probabilistic approach for predicting the risk of an indoor moving object in real-time. We propose a probabilistic flow graph (PFG) and an aggregated probabilistic flow graph (APFG) that capture the historical object transitions and the durations of the transitions. In the graphs, the probabilistic information is stored in a set of histograms. Then we use the flow graphs for obtaining a risk score of an online object and use it for predicting its riskiness. The paper reports a comprehensive experimental study with multiple synthetic data sets and a real baggage tracking data set. The experimental results show that the proposed method can identify the risky objects very accurately when they approach the bottleneck locations on their paths and can significantly reduce the operation cost.
Speaker: Tanvir Ahmed
New Daisy Visitor: Analysis of big traffic data
19 Aug 2015
Some time ago, Professor Qiang Lu acquired a large and unique traffic camera data set. He and his Chinese collaborators believed that this data set contains very valuable information, including information about the practice of using fake license plates for the purpose of avoiding ticketing. Having received the data, he then needed to find out […]
Two New Associate Professors in Daisy
11 Aug 2015
Daisy faculty members, Katja Hose and Bin Yang, have been appointed Associate Professors in Computer Science at Aalborg University in August 2015.