PhD Forum
Xingzhe Xie, Francis Deboeverie, Mohamed Eldib, Wilfried Philips, Hamid Aghajan · 2014
Behavior analysis plays an important role in the field of Smart Homes (SH). In this work, we present to analyze the behavior patterns of the elderly person using statistical features extracted from the tracking results of a very low-resolution camera system. Firstly, the low-precision tracking results are prepossessed to remove the bad tracks, which do not fit the walking speed of human beings. The good tracks are classified into walking tracks and staying tracks according to the position variance of their tracking points. Secondly, the statistical features, such as the time of getting up and going to bed, the walking distance over a day, and the number of tracks detected at specific area, are extracted as the description of the behaviors at each day. At last, these features are clustered into different behaviors patterns using a Random Sample Consensus (RANSAC)-principle method. The initial results demonstrates that our method is able to detect the behavior patterns.