Wednesday, 28 March 2012

User Attention Detection

The project has changed a bit, to enhance the current avatar software ( I am going to work on a method to detect the attention of a user at the Kinect Kiosk.  This will replace the kinect mouse cursor.

At the moment the avatar software installed can respond to user input using an AI system that can be trained to respond to certain questions.

The supervisor would like the system to greet users that show some attention to the kiosk and wish to use it. I have done some research into human detection in video, as well as face detection in video. The primary method will be to use some feature detection algorithms that will detect human presence, and compare this with how we expect a user using the system to appear.

Several feature detection methods and algorithms can be used such as Face detection, Knowledge-based, Feature invariant, Template matching, Appearance-Based and Movement detection.[1]

Furthermore, for real time processing, classifiers can be stacked, using less accurate, yet faster methods first to detect where faces certainly aren't, then using more robust methods to detect actual faces.[2]

[1]  Muhammad Usman Ghani Khan, Atif Saeed, Human detection in videos, Theoretical and Applied Information Technology, Volume 5, Issue 2, Februaru 2009, ISSN 1992-8645
Keywords: Video processing, Computing vision, Human detection, Face recognition

[2]  Yun Tie, Ling Guan, Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques, Pattern Recognition, Volume 42, Issue 9, September 2009, Pages 1859-1868, ISSN 0031-3203, 10.1016/j.patcog.2008.11.026.
Keywords: Local normalization; Optimal adaptive correlation (OAC); Adaboost algorithm

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