Digital characterization using computer vision in real-time

Robert Ravnik (2009) Digital characterization using computer vision in real-time. EngD thesis.

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Abstract

A computer system for digital characterization is described. It is an intelligent system for displaying selected visual information on a computer screen. The system tracks and characterizes the viewers by analyzing the images of their faces taken by a camera attached to the screen, using the computer vision methods in real time. It also performs logging and analysis of recorded data. Main components of the system are the player and the main server. The player performs characterization of the viewers which can be used for adjusting the information on the screen. The main server performs selection of the presented information and manipulates the data. The problem of face detection and tracking is solved by applying the methods of AdaBoost and Lucas-Kanade optical flow. Detection and tracking of several observers is performed in real time. In face characterization we use the Principal Component Analysis (PCA) and the data mining environment Orange. Characterization of the observer's gender from their face is specially treated. The following methods of machine learning are applied and inter-compared in the Orange environment: naive Bayes, K-nearest neighbors, classification tree and random forest. A part of FERET face image library is used as learning and test sets. Selected methods are implemented for real time application on a PC. Gender of an observer can be determined in 17,9 ms with 83,3% reliability using random forest classifier. A web application for managing of the system and for generating reports is developed, including statistical analysis and visualization of data.

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