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Báo cáo hóa học: " Research Article 3D Shape-Encoded Particle Filter for Object Tracking and Its Application to Human Body Tracking"

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 3D Shape-Encoded Particle Filter for Object Tracking and Its Application to Human Body Tracking | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008 Article ID 596989 16 pages doi 10.1155 2008 596989 Research Article 3D Shape-Encoded Particle Filter for Object Tracking and Its Application to Human Body Tracking H. Moon1 and R. Chellappa2 1 VideoMining Corporation 403 South Allen Street Suite 101 State College PA 16801 USA 2 Department of Electrical and Computer Engineering University of Maryland College Park MD 20742 USA Correspondence should be addressed to H. Moon hmoon@videomining.com Received 1 February 2007 Revised 14 July 2007 Accepted 25 November 2007 Recommended by Maja Pantic We present a nonlinear state estimation approach using particle filters for tracking objects whose approximate 3D shapes are known. The unnormalized conditional density for the solution to the nonlinear filtering problem leads to the Zakai equation and is realized by the weights of the particles. The weight of a particle represents its geometric and temporal fit which is computed bottom-up from the raw image using a shape-encoded filter. The main contribution of the paper is the design of smoothing filters for feature extraction combined with the adoption of unnormalized conditional density weights. The shape filter has the overall form of the predicted 2D projection of the 3D model while the cross-section of the filter is designed to collect the gradient responses along the shape. The 3D-model-based representation is designed to emphasize the changes in 2D object shape due to motion while de-emphasizing the variations due to lighting and other imaging conditions. We have found that the set of sparse measurements using a relatively small number of particles is able to approximate the high-dimensional state distribution very effectively. As a measure to stabilize the tracking the amount of random diffusion is effectively adjusted using a Kalman updating of the covariance matrix. For a complex problem of human body tracking we have successfully