Abstract
This article is dedicated to the research of video motion segmentation algorithms based on optical flow equations. First, some mainstream segmentation algorithms are studied, and on this basis, a segmentation algorithm for spectral clustering analysis of athletes’ physical condition in training is proposed. After that, through the analysis of the existing methods, compared with some algorithms that only process a single frame in the video, this article analyzes the continuous multiple frames in the video and extracts the continuous multiple frames of the sampling points through the Lucas-Kanade optical flow method. We densely sampled feature points contain as much motion information as possible in the video and then express this motion information through trajectory description and finally achieve segmentation of moving targets through clustering of motion trajectories. At the same time, the basic concepts of image segmentation and video motion target segmentation are described, and the division standards of different video motion segmentation algorithms and their respective advantages and disadvantages are analyzed. The experiment determines the initial template by comparing the gray-scale variance of the image, uses the characteristic optical flow to estimate the search area of the initial template in the next frame, reduces the matching time, judges the template similarity according to the Hausdorff distance, and uses the adaptive weighted template update method for the templates with large deviations. The simulation results show that the algorithm can achieve long-term stable tracking of moving targets in the mine, and it can also achieve continuous tracking of partially occluded moving targets.
Highlights
Under visible light irradiation, objects in the surrounding environment form images on the retina of the human eye, which are converted by photoreceptor cells into nerve impulse signals, which are transmitted to the cerebral cortex via nerve fibers for processing and understanding
A video motion segmentation algorithm based on spectral clustering is proposed, which uses the observation that the trajectories of moving objects in the video are similar to perform clustering analysis on the trajectories of densely sampled points to realize the segmentation of moving objects
The moving target detected by the background difference method is prone to “tailing” and is more sensitive to the dynamic changes of the background; the frame difference method has a small amount of calculation, and the detected moving target has “holes”; the optical flow method is very sensitive to the movement of the camera
Summary
Objects in the surrounding environment form images on the retina of the human eye, which are converted by photoreceptor cells into nerve impulse signals, which are transmitted to the cerebral cortex via nerve fibers for processing and understanding. A video motion segmentation algorithm based on spectral clustering is proposed, which uses the observation that the trajectories of moving objects in the video are similar to perform clustering analysis on the trajectories of densely sampled points to realize the segmentation of moving objects. It is proposed to use the classic clustering algorithm to cluster the similarity matrix while adding the structural information of the moving objects in the video to improve the clustering effect.
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