Research Of Differential Optical Flow Estimation Based On Color And Technology Of Moving Object Detection

It is a hot topic in computer vision field that determining the structure of moving object in 3D space or the relative motion parameters between the viewer and the object using image sequence. And it will be 2D motion when 3D motion is projected onto the 2D image surface. This motion will appear as the flow state of 2D brightness pattern, which is called optical flow. The optical flow field is a kind of 2D flow velocity fields. The computation of optical flow field is an important task of low level vision computation technology.The researching of optical flow computation technology has lasted for thirty years. And many effective computation methods have been produced. Many algorithms such as differential optical flow algorithm, matching optical flow algorithm, energy-based optical flow algorithm, phase-based optical flow algorithm and wavelet optical flow algorithm are proposed and developed one after the other. Among all of these algorithms, differential optical flow method is used broadly because it has the complete mathematics theories, can be realized easily and has high precision of estimation. But these algorithms metioned above are all based on the gray information and the color information is ignored dure the procedure of computation. Optical flow estimation is an ill-posed problem. The additional constraint of color information can be used to solve caperture problem. Color optical flow estimation method can be classified as gray consistency method and color consistency method. The experiments show that the precision of optical flow estimation is better than gray method. On all accounts, optical flow estimation based on color information is still immature. And it is a valuable topic that using the color information to improve the performance of existing optical flow estimation method. In this thesis, we reviewed the classic gray and color optical flow method, and focused on improve the performance of traditional gray optical flow methods using color information. At the same time, we also discussed the method of moving object detection using optical flow.The innovations of this thesis are as the follows:1. Based on the high order optical flow method, a new optical flow estimation method based on color gradient consistency is proposed. This method can be regarded as a second order optical flow method. And its first order gradient is just employed color vector gradient. The gradient constraint equation can be produced based on assumption of color gradient consistency and global smoothness constraint is used to solve optical flow. Finally, the numerical experiments were performed. The experiments show that the method is effective.2. Based on the characteristic of classic color optical flow method and global smoothness optical flow method, a new method fused color and gray optical flow method is proposed. The reliability of color optical flow is judged by condition number of matrix. The method adopts the smooth optical flow value to replace the unreliable color optical flow so that we can get the fixed optical flow field.At the same time, the color local model and color global model have been discussed in this thesis. The local model uses local constraint to improve the precision of estimation and has robustness to against the noise. The global model can produce density optical flow field and blur the edge of moving object at one time. Refereced the fuse method discussed above, three fuse models of color optical flow computation are proposed. And the experiments were exployed to contrast. The experiments show that the method is effective.3. The thesis reviewed the basic theory of wavelet and the problem in optical flow computation. Then complex wavelet is discussed and it can be used to overcome the effect of phase concussion. A method which used color information to improve the performance of complex wavelet is proposed. Multi-channel color information is used in this method to extend the basic complex wavelet optical flow equation and condition number is used to judge the reliability of every channel. The best stablest channel is selected to compute optical flow using complex wavelet. Finally, the numerical experiments were performed. The experiments show that the method is effective.4. A method of moving object detection based on optical flow field and level set is proposed. Epipolar constraint or standardization optcal flow field is used to determeming the number and initial motion region. Then Kmeans algorithm is used to get the region of moving object. However, the algorithm can not get the accurate edge of moving object because the error of segmentation and computation of optical flow. So level set method based on spatial information is adopted to get the final segmentation result. Color vector gradient is used to define the evolution ending function and fast marching method is used to improve the performance of level set method. Finally, the experiments which include single object, multi-object, state background and moving background were performed. The experiments show that the method is effective.