tailieunhanh - Báo cáo hóa học: " Research Article Audiovisual Head Orientation Estimation with Particle Filtering in Multisensor Scenarios"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Audiovisual Head Orientation Estimation with Particle Filtering in Multisensor Scenarios | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 276846 12 pages doi 2008 276846 Research Article Audiovisual Head Orientation Estimation with Particle Filtering in Multisensor Scenarios Cristian Canton-Ferrer 1 Carlos Segura 2 Josep R. Casas 1 Montse Pardas 1 and Javier Hernando2 1 Image Processing Group Universitat Politecnica de Catalunya 08034 Barcelona Spain 2 TALP Research center Universitat Polit ecnica de Catalunya 08034 Barcelona Spain Correspondence should be addressed to Cristian Canton-Ferrer ccanton@ Received 1 February 2007 Accepted 7 June 2007 Recommended by Enis Ahmet Cetin This article presents a multimodal approach to head pose estimation of individuals in environments equipped with multiple cameras and microphones such as SmartRooms or automatic video conferencing. Determining the individuals head orientation is the basis for many forms of more sophisticated interactions between humans and technical devices and can also be used for automatic sensor selection camera microphone in communications or video surveillance systems. The use of particle filters as a unified framework for the estimation of the head orientation for both monomodal and multimodal cases is proposed. In video we estimate head orientation from color information by exploiting spatial redundancy among cameras. Audio information is processed to estimate the direction of the voice produced by a speaker making use of the directivity characteristics of the head radiation pattern. Furthermore two different particle filter multimodal information fusion schemes for combining the audio and video streams are analyzed in terms of accuracy and robustness. In the first one fusion is performed at a decision level by combining each monomodal head pose estimation while the second one uses a joint estimation system combining information at data level. Experimental results conducted over the CLEAR 2006 evaluation database

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