Gaussian propagation model based dense optical flow for objects tracking
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
A novel evidence based model for detecting dangerous situations in level crossing environments
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Motion vector estimation is an important parameter for video segmentation. Effective video compression can be achieved by choosing a correct approach for the calculation of motion vector. Here in this paper we propose an optical flow motion vector estimation through iterative Lucas-Kanade Pyramidal implementation for both large & small motion In image pyramid representation a group of pixel information is gradually reduced to a value of one pixel information for both current & reference frame. The velocity & displacement at each pixel is obtained by using Lucas-Kanade equations. The original image is recovered by warping reference frame towards current frame using flow vectors i.e. velocity & displacement by using image warping techniques. The process is repeated until convergence. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences.