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|Title:||Fast detection of frequent change in focus of human attention|
|Source:||Hu, N.,Huang, W.,Ranganath, S. (2005). Fast detection of frequent change in focus of human attention. Lecture Notes in Computer Science 3368 : 216-230. ScholarBank@NUS Repository.|
|Abstract:||We present an algorithm to detect the attentive behavior of persons with frequent change in focus of attention (FCFA) from a static video camera. This behavior can be easily perceived by people as temporal changes of human head pose. Here, we propose to use features extracted by analyzing a similarity matrix of head pose by using a self-similarity measure of the head image sequence. Further, we present a fast algorithm which uses an image vector sequence represented in the principal components subspace instead of the original image sequence to measure the self-similarity. An important feature of the behavior of FCFA is its cyclic pattern where the head pose repeats its position from time to time. A frequency analysis scheme is proposed to find the dynamic characteristics of persons with frequent change of attention or focused attention. A nonparametric classifier is used to classify these two kinds of behaviors (FCFA and focused attention). The fast algorithm discussed in this paper yields real-time performance as well as good accuracy. © Springer-Verlag Berlin Heidelberg 2005.|
|Source Title:||Lecture Notes in Computer Science|
|Appears in Collections:||Staff Publications|
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