Abstract
The smoothness parameter is used to balance the weight of the data term and the smoothness term in variational optical flow model, which plays very significant role for the optical flow estimation, but existing methods fail to obtain the optimal smoothness parameters (OSP). In order to solve this problem, an adaptive smoothness parameter strategy is proposed. First, an amalgamated simple linear iterative cluster (SLIC) and local membership function (LMF) algorithm is used to segment the entire image into several superpixel regions. Then, image quality parameters (IQP) are calculated, respectively, for each superpixel region. Finally, a neural network model is applied to compute the smoothness parameter by these image quality parameters of each superpixel region. Experiments were done in three public datasets (Middlebury, MPI_Sintel, and KITTI) and our self-constructed outdoor dataset with the proposed method and other existing classical methods; the results show that our OSP method achieves higher accuracy than other smoothness parameter selection methods in all these four datasets. Combined with the dual fractional order variational optical flow model (DFOVOFM), the proposed model shows better performance than other models in scenes with illumination inhomogeneity and abnormity. The OSP method fills the blank of the research of adaptive smoothness parameter, pushing the development of the variational optical flow models.
Highlights
Motion detection [1, 2] is a research hotspot of image processing, which is widely applied in motion segmentation [3], target tracking [4], and video [5] surveillance [6]
Variational optical flow algorithm [8] is one of the most popular optical flow methods, which consists of three parts: data term, smoothness term, and smoothness parameter
In order to improve the performance of the variational optical flow algorithm, many improvements have been done to the data and smoothness term to solve the large displacement problem [9], enhance the robustness against noise and illumination changes [10,11,12], maintain the discontinuity between different motion regions [13], highlight the contour of motion regions [14,15,16], and deal with occlusion problems [17, 18]
Summary
Motion detection [1, 2] is a research hotspot of image processing, which is widely applied in motion segmentation [3], target tracking [4], and video [5] surveillance [6]. 2. Related Work e smoothness parameter adjustment strategy for the variational optical flow model was first proposed by Nagel in 1986. In [21], a smoothness weight selection method was proposed; the method uses a blurring operator to calculate the weighted distance, but the spatially varying character needs to be adjusted before it can be applied to variational optical flow model. 5. The Superpixel Segmentation e image quality parameters in a local region are considered to be the same if the RGB values of pixels are same or similar. It can be seen that some of the edge points of superpixel regions have been classified correctly after processing by the local membership function
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