Omnibearing vision based method for identifying and positioning dynamic target

Abstract

The invention relates to an omnibearing vision based method for identifying and positioning a dynamic target, belonging to the technical field of dynamic image analysis. The method comprises the following steps of: 1, acquiring an omnibearing vision sequence image, and preprocessing the omnibearing vision sequence image to obtain a binary image separating a moving target and a background area; 2, searching a local area by an optical flow method, matching feature points between adjacent frames of the image, and detecting the moving target of an image sequence; 3, estimating the moving state of the moving target by a particle filtering algorithm, and predicting the parameter of the moving target in a subsequent frame so as to complete a tracking process. The invention can obviously reduce the calculating amount and enhance the accuracy by identifying and positioning the dynamic target by the method.

Description

technical field [0001] The invention belongs to the technical field of dynamic image analysis, and relates to a target recognition and positioning method based on omnidirectional vision. Background technique [0002] The basic task of dynamic image analysis is to detect motion information from image sequences, identify and track moving objects. It involves image processing, image analysis, artificial intelligence and pattern recognition, computer vision and other research fields. It is a very active branch in the neighborhood of image processing and computer vision. application, so its research has very important practical significance. [0003] In order to identify moving targets and track them, people usually use the optical flow field method to extract the optical flow field from the real-time collection of image sequences containing moving targets, filter out the moving target area with large optical flow and calculate the moving target’s distance. Velocity vector, so ...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the amount of calculation is large and the real-time performance is not strong.

(2) Use the characteristic optical flow technology to find the feature points in the image for matching, and obtain the sparse optical flow field. The real-time performance of this method of extracting the target has been improved, but the amount of information is insufficient, which may easily cause the target to be missed.

In terms of target tracking, the previous practice often separates them, and then tracks based on the characteristics of the target after the detection is realized. This increases the complexity of the algorithm processing and brings complexity when the target enters and exits. process of

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