Image Segmentation Processing Method Based on Area Matching Optimal K-Means Clustering Algorithm

Abstract

The invention relates to an image segmentation processing method based on an area matching optimization K-means clustering algorithm, comprising steps of firstly extracting vehicle feature points of front and back frame images, and then comparing an area overlapping situation of front and back frame vehicles, extracting positions of the feature points in an area overlapping region and the positions of the rest feature points, respectively calculating a mean value of the two groups of the feature points as two types of initial clustering central points to be segmented, and then implementing K-mean segmentation, correcting a classification situation of the feature points in the area overlapping region according to an output clustering result, and meanwhile, judging whether the clustered vehicles are reasonable or not; and if not, re-clustering the clustering result and recounting clustering centres, ending clustering segmentation until the reasonable vehicles are found, and then feeding back a tracking result. The method is on the basis of area matching optimization, and adopts fixed clustering numbers to implement the segmentation; and vehicle targets obtained by the K-mean segmentation do not need the next round of matching treatment, thereby a processing speed is accelerated, and time is saved.

Description

technical field [0001] The invention relates to the technical field of video detection, in particular to the technical field of video image target tracking, in particular to an image segmentation processing method based on an area matching optimized K-means clustering algorithm. Background technique [0002] The video vehicle tracking and recognition technology must first segment the moving target in the video image, and then the target can be tracked. In the case of very congested traffic, the target vehicle may overlap with other vehicles. At this time, it is difficult for traditional segmentation algorithms to segment the connected area containing a single vehicle. In order to overcome this problem, more and more methods of tracking vehicle feature points are used to track the target vehicle. At this time, it is necessary to segment the cohesive multi-vehicle feature points to improve tracking efficiency. [0003] The traditional K-means clustering algorithm does not use...

AI Technical Summary

Problems solved by technology

[0003] The traditional K-means clustering algorithm does not use a random method to select the initial clustering center, and the clustering results may be different depending on the selected points. Such dependence leads to instability of the clustering results, and it is easy to fall into a local optimum Rather than the global optimal clustering result; and the clustering algorithm is very sensitive to noise points and outliers; the clustering result depends on the setting of the initial value, but the selection of the k value (number of clusters) often needs to go through many experiments to find the best value

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