Coordinate extracting algorithm of lacertilian-imitating suborder chamaeleonidae biological vision

Abstract

The invention belongs to the field of computer vision and particularly relates to a computer vision algorithm capable of performing the target tracking, location and camera marking at the same time. The invention provides a camera marking method, or a target tracking and locating method, applied to an active three-dimensional vision system of a lacertilian-imitating chamaeleonidae suborder biological vision system structure. The coordinate extracting algorithm is mainly characterized in the camera marking; the tracking and location of an interested target are carried out synchronously; the marking process of the camera does not need the continuous motionlessness of the interested target and also does not need the execution before the target tracking and location. The continuous marking process of the adopted marking method is a discrete process; by using the target to perform the camera marking on the static state in the operation process of the vision algorithm, the requirement of the camera marking is reduced, the synchronous execution of the vision algorithm and the marking process is achieved. For the property of self-marking, the manual marking work is reduced, and the preparatory work of the algorithm is largely simplified.

Description

technical field [0001] The invention belongs to the category of computer vision, in particular to a computer vision algorithm capable of simultaneously performing target tracking, positioning and camera calibration. The biological system that it imitates is the visual system of the lizard suborder evasion family organisms. Background technique [0002] Computer vision is an important research direction in the field of artificial intelligence. Compared with other information detection methods, the detection device based on the concept of computer vision has the characteristics of large amount of information, low cost, no interference to the environment, and easy control. There are many ways to structure the vision system, which can be divided into: monocular vision, binocular vision, and multi-eye vision based on the number of cameras; it can be divided into: active vision and passive vision based on mechanical structure constraints; The degree of association can be divided ...

AI Technical Summary

Problems solved by technology

However, the conventional calibration method requires execution before the vision algorithm runs, and requires the target of interest to be stationary during the calibration process.

These two requirements make the use of active vision system cumbersome, especially when the camera in the vision system is changed frequently, or the system uses a zoom camera, which makes the vision processing process extremely cumbersome

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