THE VERIFICATION AND EXPERIMENT OF OPTICAL FLOW METHOD

Introduction. The quantitative measurement of flow velocity field possesses fundamental and key importance for understanding complex flow phenomenon and physical nature in the research of aerodynamics, at the same time, the demand also stimulates the development of aerodynamic test technology level. Traditional flow measurement methods mostly adopt single point measurement or invasive measurement techniques [1], cannot meet the needs of complex flow situation and global measurement. In recent decades, depending on particle image and cross correlation method to acquire velocity vector field, the Particle Image Velocimetry had achieved considerable development and hold application advantages of non-invasive measurement, global measurement and high accuracy [2]. The velocity vector obtained by PIV is the average velocity of interpretation window, so the variation of velocity field in the interpretation window is ignored. During the exposure interval, the displacement of particle, which follows the nyquist criterion, should be less than half of the size of interpretation window so that the spatial resolution of PIV is limited by certain restrictions. The accuracy of velocity vector field obtained by PIV is also restricted in the region with large velocity gradient. Images of object recorded by camera are essentially distribution of reflected light from object. When human’s eyes observe moving object, the motion scene of the object will show a series of continuous varying images passing through the retina (i.e. the image plane), just like a stream of light, which is called “optical flow”, passing through the retina. Under the condition of illumination, the surface grayscale of an object will presents certain spatial distribution, which is called grayscale pattern, and the So-called “optical flow” refers to the motion of the grayscale pattern. The optical flow image represents the spatial distribution of some kind of physical quantity, such as temperature, concentration, density or the grayscale space distribution. The optical flow coming from the movement between observer and observed object can reveal the motion information of the observed object. Particle images record reflected light from particle swarm and show particle concentration distribution, so velocity vector field by PIV method is essentially velocity vector field of “optical flow”. Horn and Schunck creatively studied optical flow in 1980s [3]. Horn et al thought that the optical flow field caused by moving object should be continuous and smooth, namely the grayscale variation projected on the image should be smooth because velocities of adjacent points on the same object should be similar. Based on this idea, additional velocity smoothness constraint, i.e. global smoothness constraint can be added to optical flow field, so the calculation problem of optical flow field could be translated into the calculus problem of variation and the motion estimation of pixel scale would be obtained. Subsequently, the optical flow technology attracted attention of researchers [4–6], some calculating methods, such as image correlation method, energy method [7], phase method [8] and neutral dynamics method, are proposed. Aiming at the difficult mentioned by TianShu Liu [9] and faced in differential method proposed by Horn when calculating the larger pixel displacement, Corpetti put forward using optical flow in the form of integral equation instead of the differential form of variational equation [10], Ruhnau etc applied Gaussian filter to create the multi-resolution strategy of binary image pyramid [11]. These algorithms have their own advantages, but due to a certain scope of application and algorithm complexity, so it is unrealistic for these algorithms to get a wide range of applications relative to the differential method.