Optical Flow Matching Research Articles

A novel particle tracking velocimetry method for complex granular flow field**Project supported by the National Natural Science Foundation of China (Grant Nos. 11572201 and 91634202).

Particle tracking velocimetry (PTV) is one of the most commonly applied granular flow velocity measurement methods. However, traditional PTV methods may have issues such as high mismatching rates and a narrow measurement range when measuring granular flows with large bulk density and high-speed contrast. In this study, a novel PTV method is introduced to solve these problems using an optical flow matching algorithm with two further processing steps. The first step involves displacement correction, which is used to solve the mismatching problem in the case of high stacking density. The other step is trajectory splicing, which is used to solve the problem of a measurement range reduction in the case of high-speed contrast The hopper flow experimental results demonstrate superior performance of this proposed method in controlling the number of mismatched particles and better measuring efficiency in comparison with the traditional PTV method.

Disparity estimation in stereo video sequence with adaptive spatiotemporally consistent constraints

Numerous stereo matching algorithms have been proposed to obtain disparity estimation for a single pair of stereo images. However, simply even applying the best of them to temporal frames independently, i.e., without considering the temporal consistency between consecutive frames, may suffer from the undesirable artifacts. Here, we proposed an adaptive, spatiotemporally consistent, constraints-based systematic method that generates spatiotemporally consistent disparity maps for stereo video image sequences. Firstly, a reliable temporal neighborhood is used to enforce the “self-similarity” assumption and prevent errors caused by false optical flow matching from propagating between consecutive frames. Furthermore, we formulate the adaptive temporal predicted disparity map as prior knowledge of the current frame. It is used as a soft constraint to enhance the temporal consistency of disparities, increase the robustness to luminance variance, and restrict the range of the potential disparities for each pixel. Additionally, to further strengthen smooth variation of disparities, the adaptive temporal segment confidence is incorporated as a soft constraint to reduce ambiguities caused by under- and over-segmentation, and retain the disparity discontinuities that align with 3D object boundaries from geometrically smooth, but strong color gradient regions. Experimental evaluations demonstrate that our method significantly improves the spatiotemporal consistency both quantitatively and qualitatively compared with other state-of-the-art methods on the synthetic DCB and realistic KITTI datasets.

Application of matching optical flow method in obstacle avoidance of mobile

Optical flow method is an important method to study the obstacle avoidance of mobile robot. Optical flow calculation is the core problem of robot obstacle recognition, which can be used to realize the obstacle avoidance by using the balance strategy after calculating the optical flow. In this paper, a point matching method based on optimal estimation is used. It is based on a certain similarity between the two points of the optimal estimation of the characteristics, so as to find out the characteristics of the points and calculate the optical flow method. This method has a good performance in the research of moving target detection. In this paper, we apply it to the research of obstacle avoidance, which can improve the speed of optical flow calculation and make the obstacle avoidance process faster.