• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.143-148
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• 2014

Particle filter algorithm has been proven very successful for non-linear and non-Gaussian estimation problem and thus it has been widely used for object tracking for video signals. If the object moves significantly, particle filter needs very large number of particles to track object and this results high computational cost. In this paper, modified particle filter by adopting motion vector is proposed for tracking vehicle in low frame rate(LPR) video input, which the object moving significantly and randomly between consecutive frames. In the proposed algorithm, motion vector is applied in selection and observe step. The experimental result shows that the proposed particle filter can track vehicle successfully in the case when previous one fails. And it also shows the propose method increases the precision of tracking.

• Journal of IKEEE
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• v.17 no.4
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• pp.459-468
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• 2013

Under nonlinear characteristics of frames, we propose the frame interpolation using GRNN to enhance the visual picture quality. By full search with block size of 128x128~1x1 to reduce blocky artifact and image overlay, we select the frame having block of minimum error and re-estimate the nonlinear moving vector using GRNN. We compare our scheme with forward(backward) motion compensation, bidirectional motion compensation when the object movement is large or the object image includes zoom-in and zoom-out or camera focus has changed. Experimental results show that the proposed method provides better performance in subjective image quality compared to conventional MCFI methods.