Motion estimation is a means, which consists in studying the displacement of objects in a video sequence, seeking the correlation between two successive frames, to predict the change in the contents position. Motion estimation is becoming a progressively significant requirement in a variety of applications such as medicine, robotics and video compression. In recent years, wavelets are effective tools for motion estimation, but the DWT (Discrete Wavelet Transform) will suffer from problems like translation sensitivity, poor directionality and absence of phase information. These three disadvantages make classical wavelets incapable of calculating motion in complex sequences (contain several directions.). In order to improve these negative aspects, we will choose geometric wavelet. Therefore, our objective is to propose a method capable of estimating the motion in terms of performance (speed and accuracy). This method will be based on the geometric wavelet transform and more precisely on the Contourlet transform. This work consists of two parts: in the first stage, the denoising process is examined by the Contourlet transform to ensure the precision of motion; in the second phase, we applied the iterative method of Horn and Schunck to calculate the motion in order to guarantee good speed. Comparative experimental results of artificial sequences show that the proposed algorithm obtains considerably better performance than several state-of-the-art methods.
Fast motion vector estimation using multiresolution-spatio-temporal correlations
