Camshift (continuously adaptive mean-shift) robot fish tracking method based on embedded Kalman filter

A technology of Kalman filter and robotic fish, which is applied in the direction of instrumentation, image data processing, calculation, etc., can solve the problems of tracking failure and large tracking target error, and achieve the effect of improving accuracy and enhancing accuracy

Pending Publication Date: 2017-05-31
BEIJING INSTITUTE OF TECHNOLOGYGY +2
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Problems solved by technology

However, in the case of complex background and sudden and fast movement of the target, there may be problems with large error in tracking the target or even tracking failure.

Method used

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  • Camshift (continuously adaptive mean-shift) robot fish tracking method based on embedded Kalman filter
  • Camshift (continuously adaptive mean-shift) robot fish tracking method based on embedded Kalman filter
  • Camshift (continuously adaptive mean-shift) robot fish tracking method based on embedded Kalman filter

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Embodiment Construction

[0025] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0026] The invention provides a Camshift robotic fish tracking method based on an embedded Kalman filter, adopts the H component histogram based on the HSV color space as a matching standard, and adds The Kalman filter is used to complete the general prediction of the target position in the current frame image by using the optimal position of the target in the previous frame, and then the MeanShift algorithm is used to match and find the target point in the neighborhood of the predicted position, and the target point is used as the observation value Correct the prediction of the Kalman filter to obtain the optimal estimate of the target position, and then continue to iterate the obtained optimal estimate of the target position for the next position prediction. The Kalman filter only needs to know the state vector at the previous moment and the observed val...

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Abstract

The invention discloses a Camshift (continuously adaptive mean-shift) robot fish tracking method based on an embedded Kalman filter. The Camshift robot fish tracking method is suitable for complicated underwater environments of robot fishes, the accuracy of the robot fish tracking quick motion is improved, and the real-time performance is good. The Camshift robot fish tracking method comprises the following steps of firstly, utilizing the Kalman filter to predict the possible position of a motion object in the next frame of image; then, using Camshift to search in the relative reduction range, so as to effectively enhance the accuracy of tracking a quick motion object; then, utilizing the observing value obtained by the Camshift to correct the observing value obtained by the Kalman filter, so as to further improve the accuracy of target tracking. Compared with the prior art, the Camshift robot fish tracking method has the advantages that the real-time performance and accuracy of the target tracking algorithm can be comprehensively considered, and the purpose of the robot fish accurately tracking the target in real time is realized.

Description

technical field [0001] The invention relates to the technical field of intelligent control, in particular to a method for tracking robotic fish based on Camshift embedded with a Kalman filter. Background technique [0002] With the continuous deepening of the development of marine resources, the cooperative control system of bionic robotic fish has attracted attention. As an important part of the cooperative control system of bionic robotic fish, the vision subsystem is the only source of information for the decision-making subsystem. The visual tracking algorithm determines the goal. Fast accuracy and real-time tracking. [0003] Machine vision first appeared in a 1975 collection of papers edited by Winston. Professor Marr from the United Kingdom created a new visual theory research group at the Massachusetts Institute of Technology (MIT) in 1973. In 1977, he proposed a new computer vision theory - Marr visual theory, which was introduced in the 1980s It has become a very...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/207
CPCG06T2207/10016
Inventor 郭树理韩丽娜袁振兵王稀宾崔伟群王春喜司全金李铁岭刘源黄剑武王彬华郭芙苏曲大成
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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