Binocular vision-based target tracking method for hot-line working robot

Abstract

The invention provides a binocular vision-based target tracking method for a hot-line working robot. A target area is described with color and texture fused features, a color and texture fused feature histogram is used as a feature histogram of a target model, the target area of a left camera image is tracked with a mean shift-based target tracking method, and the position and dimension information of a target in a current-frame image of a left camera are acquired; the position of the target in a right camera image is acquired with a template matching method, and coordinates of the target in a world coordinate system are calculated according to the binocular stereo vision three-dimensional measurement principle; by the aid of the dimension information of the target in the current-frame image of the left camera, the dimensions of the target in the left camera image are estimated with a dimension estimating method based on the image forming principle and moving constraints, and dimension correction is performed on a target area, required to be tracked, in a next-frame image of the left camera. With the method, the target identification rate of the hot-line working robot during working can be increased.

Description

technical field [0001] The invention belongs to the field of electric power technology, and in particular relates to a binocular vision-based target tracking method for a live working robot. Background technique [0002] With the vigorous development of robot technology, the status of robots in modern production and life is becoming more and more important. Introducing live working robots into the power industry to replace manual power maintenance and repair work can effectively avoid casualties during live work, and can greatly improve the operating efficiency of power maintenance and repair. [0003] Using robots for live work, operators can use the working environment information collected and fed back by the vision system to remotely monitor the robot to complete live work. Among them, target tracking is used to distinguish the target from the background, so as to realize the precise positioning of the target. However, in the live work site, the environment is more com...

AI Technical Summary

Problems solved by technology

However, in the live work site, the environment is relatively complex, there are many equipment and appliances, and these equipment and appliances have a single color, which is not easy to distinguish from the background environment. These factors make it difficult to track targets

Currently commonly used tracking methods such as the target tracking method based on mean shift generally use the color probability density distribution as the target description, and the recognition degree of the target is not high when the color characteristics of the live working environment are not obvious. When the illumination changes in the scene , the color distribution of the target will also change, which will lead to the instability of the model and the failure of target tracking; and this method is poorly adaptable to the target scale change. When the scale of the target in the image changes, the target tracking cannot Work properly

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