• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.262-269
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• 2010

This paper presents a probabilistic head tracking method, mainly applicable to face recognition and human robot interaction, which can robustly track human head against various variations such as pose/scale change, illumination change, and background clutters. Compared to conventional particle filter based approaches, the proposed method can effectively track a human head by regularizing the sample space and sequentially weighting multiple visual cues, in the prediction and observation stages, respectively. Experimental results show the robustness of the proposed method, and it is worthy to be mentioned that some proposed probabilistic framework could be easily applied to other object tracking problems.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1421-1431
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• 2005

There are two methods in 3-D sound reproduction: a surround system, like 3.1 channel method and a binaural system using 2-channel method. The binaural system utilizes the sound localization principle of a human using two ears. Generally, a crosstalk between each channel of 2-channel loudspeaker system should be canceled to produce a natural 3-D sound. To solve this problem, it is necessary to trace a head movement. In this paper, we propose a new algorithm to correctly trace the head movement of a listener. The Proposed algorithm is based on the detection of face and eye. The face detection uses the intensity of an image and the position of eyes is detected by a mathematical morphology. When the head of the listener moves, length of borderline between face area and eyes may change. We use this information to the tracking of head movement. A computer simulation results show That head movement is effectively estimated within +10 margin of error using the proposed algorithm.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.243-248
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• 2013

This paper proposes an effective method using Adaptive Boosting to track a person's head in complex background. By only one way to feature extraction methods are not sufficient for modeling a person's head. Therefore, the method proposed in this paper, several feature extraction methods for the accuracy of the detection head running at the same time. Feature Extraction for the imaging of the head was extracted using sub-region and Haar wavelet transform. Sub-region represents the local characteristics of the head, Haar wavelet transform can indicate the frequency characteristics of face. Therefore, if we use them to extract the features of face, effective modeling is possible. In the proposed method to track down the man's head from the input video in real time, we ues the results after learning Harr-wavelet characteristics of the three types using AdaBoosting algorithm. Originally the AdaBoosting algorithm, there is a very long learning time, if learning data was changes, and then it is need to be performed learning again. In order to overcome this shortcoming, in this research propose efficient method using cascade AdaBoosting. This method reduces the learning time for the imaging of the head, and can respond effectively to changes in the learning data. The proposed method generated classifier with excellent performance using less learning time and learning data. In addition, this method accurately detect and track head of person from a variety of head data in real-time video images.