Abstract:

In nature scene image sequences, a covered-uncovered problem occurs when objects cover one part of background areas and uncover another because of motion. Pixels of covered-uncovered areas will lose counterpoints in consecutive images, thus the traditional optical flow methods often determine a bad optical flow at these pixels. Noise disturbance is another key problem that should be taken into account in optical flow estimation, because image data may be blurred in capture and transmission process. To avoid the determining errors arising from covered-uncovered and noise disturbance problems, a visible matrix is adopted to label the three possible states of image areas, namely covered/uncovered/visible, and a normal probability distribution is adopted to approximately describe the distribution of noises. Based on these, the arithmetic expressions of the optical flow field are deduced in the Bayes framework, and a dense optical flow field is determined using the iteration method, then CAVIAR video data are used to test the proposed algorithm, and a comparison is made between the experiment results and Negal’s method. The results show that the proposed algorithm performs better.