• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.312-315
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• 2007

운전자의 변의와 안전을 위한 장치로 센서를 차량에 많이 부착하게 되었다. 레이더도 그런 센서 중의 하나로 다른 센서들 보다 정확하게 주변상황을 인식하게 해 준다. 하지만 그런 레이더 데이터에는 차량의 정보들 뿐만 아니라 주변의 장애물, 건물 동의 원하지 않는 데이터가 같이 들어 오며 이런 물체들은 뒤에 있는 물체들을 가리기 까지 한다. 이 논문에서는 RLS(Recursive Least Square)를 사용하여 이런 가려지는 물체들의 데이터들을 버리지 않고 사용할 수 있는 알고리즘을 제안한다.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.