CN109788200B - Camera stability control method based on predictive analysis - Google Patents
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Abstract
The invention discloses a camera shooting stability control method based on predictive analysis, which comprises the following steps: firstly, continuously acquiring X, Y, Z three axial accelerations and angular velocities of the camera before a certain time point; then analyzing the motion of the camera before the certain time point according to the acquired acceleration and angular velocity in the three axial directions, and determining whether the camera is under the action of periodic external excitation before the certain time point; if yes, carrying out the next step; and finally, predicting the motion situation after a certain time point according to the motion periodicity of the image pickup equipment before the certain time point, obtaining a prediction control result according to the prediction result, and controlling the image pickup equipment. The control method of the invention mainly aims at the unstable imaging of the camera equipment under the periodic excitation, and can improve the recording effectiveness of the camera equipment under the periodic external excitation.
Description
Technical Field
The invention relates to the technical field of camera shooting control, in particular to a camera shooting stability control method based on predictive analysis.
Background
The stabilized platform is often used for ships that are prone to rolling, pitching, yawing, and the like, and can also be applied to daily-used camera stabilizing devices and the like. All the above methods are used for adjusting the posture of the equipment in real time by collecting all the information of the outside speed, the outside angular speed and the like so as to counteract the rotation or the displacement of the outside environment. The change of the speed and the angular speed of the external environment is complex and changeable, but the acceleration or the angular acceleration process in a certain direction has weak regularity or even strong regularity sometimes. For example, when the ship runs at a constant speed, the displacement-time course and the speed-time course of the ship relative to the Z axis are distributed in a similar trigonometric function; or the corresponding speed and acceleration time history of the six degrees of freedom of the movement of a certain point of the body of the person during running also have periodic regularity.
Disclosure of Invention
The invention aims to provide a shooting stability control method based on predictive analysis, which mainly aims at the problem that the imaging of a shooting device is unstable under periodic excitation and can improve the recording effectiveness of the shooting device under the periodic external excitation.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme.
A shooting stability control method based on predictive analysis comprises the following steps:
and 3, predicting the motion situation after a certain time point according to the motion periodicity of the image pickup equipment before the certain time point, obtaining a prediction control result according to the prediction result, and controlling the image pickup equipment.
The technical scheme of the invention has the characteristics and further improvements that:
(1) step 2 comprises the following substeps:
step 2a, amplifying signals and filtering to remove noise of the three axial accelerations and angular velocities acquired;
substep 2b, taking a plurality of different time period lengths, respectively and equidistantly taking n time points in each time period length, and reading the n time points into a processor;
and a substep 2c, the processor periodically judges the data of each n time points in a plurality of time periods.
Wherein the plurality of different time periods is 3.
(2) Step 3 comprises the following substeps:
the substep 3a, establishing three weighting coefficient matrixes for carrying out weighted addition on the acceleration and angular velocity data acquired in three axial directions;
substep 3b, performing curve fitting on the three groups of data obtained after weighting to obtain time histories of each axial acceleration and angular velocity in three time periods before the certain time point;
and a substep 3c, predicting the motion situation of the camera equipment after the certain time point according to the change of the axial acceleration and the angular velocity in the three time periods, obtaining a prediction control output matrix, and controlling the camera equipment to keep stable.
In sub-step 3a, the sum of the three weighting coefficient matrices is a fixed matrix.
(3) And a triaxial gyroscope is adopted to continuously acquire X, Y, Z three axial accelerations and angular velocities of the camera before a certain time point.
(4) In step 2, if not, performing step 4: and acquiring a real-time control result, combining the prediction control result with the real-time feedback control result, acquiring a final control result, and controlling the camera equipment.
The combination of the prediction control result and the real-time feedback control result specifically comprises the following steps: and respectively establishing a prediction control result weighting coefficient matrix and a real-time feedback control result weighting coefficient matrix for carrying out weighted addition on the prediction control result and the real-time feedback control result.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a shooting stability control method based on predictive analysis, which is mainly used for predicting the excitation condition after a certain time point through analyzing external excitation in a time period before the time point aiming at the imaging instability of shooting equipment under periodic excitation, and giving a control strategy based on the prediction result, thereby improving the recording effectiveness of the shooting equipment under the periodic external excitation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a predictive control data flow diagram of an embodiment of a camera stabilization control method based on predictive analysis according to the present invention;
fig. 2 is an acceleration prediction diagram in the X direction of an embodiment of the imaging stabilization control method based on prediction analysis of the present invention;
fig. 3 is a flowchart of predictive control and feedback control provided by an embodiment of the camera stabilization control method based on predictive analysis according to the present invention;
fig. 4 is a structural diagram of a three-cycle stabilizer in an embodiment of the imaging stabilization control method based on predictive analysis according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The embodiment of the invention provides a camera shooting stability control method based on predictive analysis, wherein external excitation including time history of axial angular acceleration and attitude angular velocity change is collected through a three-axis gyroscope, and sampling frequency is adjusted according to precision requirements; the collected data is amplified and filtered by a signal amplifying circuit and a filtering circuit, in this example, a MEMS (micro electro mechanical system) gyroscope chip is selected, the MEMS gyroscope chip can measure movement, acceleration and angle change in six directions, and the other part in the core of the chip can convert related sensing data into a digital format which can be recognized by a processor.
In particular, the method comprises the following steps of,
the image pickup stability control method based on prediction analysis is carried out according to the following steps:
step 2a, amplifying signals and filtering to remove noise of the three axial accelerations and angular velocities acquired;
substep 2b, taking 3 different time period lengths, respectively taking n time points in each time period length at equal intervals, and reading the n time points into a processor;
and a substep 2c, the processor periodically judges the data of each n time points in the 3 time periods.
And 3, predicting the motion situation after a certain time point according to the motion periodicity of the image pickup equipment before the certain time point, obtaining a prediction control result according to the prediction result, and controlling the image pickup equipment.
Step 3 comprises the following substeps:
the substep 3a, establishing three weighting coefficient matrixes for carrying out weighted addition on the acceleration and angular velocity data acquired in three axial directions;
substep 3b, performing curve fitting on the three groups of data obtained after weighting to obtain time histories of each axial acceleration and angular velocity in three time periods before the certain time point;
and a substep 3c, predicting the motion situation of the camera equipment after the certain time point according to the change of the axial acceleration and the angular velocity in the three time periods, obtaining a prediction control output matrix, and controlling the camera equipment to keep stable.
In step 2, if not, performing step 4: and acquiring a real-time control result, combining the prediction control result with the real-time feedback control result, acquiring a final control result, and controlling the camera equipment.
The combination of the prediction control result and the real-time feedback control result specifically comprises: and respectively establishing a prediction control result weighting coefficient matrix and a real-time feedback control result weighting coefficient matrix for carrying out weighted addition on the prediction control result and the real-time feedback control result.
By way of example, with reference to figure 1,
x, Y, Z three axial accelerations and angular velocities of the camera device before the moment t is 0 are continuously acquired; the acceleration time history in the X direction is shown in fig. 2. When t is 0, sampling is carried out on the signal before the time by a sampling module after amplification and filtering, and three different time periods (the time periods should be self-adaptive or pre-adjusted according to the period of the external environment excitation) are taken to adapt to the external excitation with different frequencies. Sampling 100 points in three time periods, wherein each time point comprises six data at most:
three data matrices are established from the three sets of point data:
each row of the three matrices in the above equation represents data of six degrees of freedom at a certain time point, and each column represents the time history of a certain degree of freedom variable in the time period. As can be seen from FIG. 2, at T1And T2The movement is not significantly periodic, but at T3The periodicity of the motion can be obviously observed, three weighting coefficient matrixes are initialized according to the periodicity strength of all the freedom degree containing variables in each time period, the three weighting coefficient matrixes meet the condition that the weighting parameters of six freedom degrees are added to form a fixed value, namely the sum of the three weighting coefficient matrixes is a fixed matrix.
By T3The weighting coefficient matrix of time is taken as an example:
when initializing the matrix, it is necessary to ensure that:
WT1+WT2+WT3＝E
when T is equal to T3When the temperature of the water is higher than the set temperature,
with a strong periodicity. At this time, WT1In (1)
While in the other two weighting matrices
This results in T being added in the next weighted addition3Pair of analysis results of
The weight of the prediction result is the largest.
Referring to the predictive control data flow diagram of FIG. 1, when predictive control is running, WT1+WT2+WT3And when the control result is not equal to 0, namely, the result of the predictive control module starts to intervene in the final control result as long as a certain degree of freedom parameter has periodicity in three time periods.
After obtaining the weighting coefficient matrixes within the three time periods and time, performing weighted addition on the acquired data, as follows:
Ans1＝T1·WT1+T2·WT2+T3·WT3
a weighted-sum data matrix is obtained as follows:
carrying out BP curve fitting on the data obtained after weighting to obtain the data outsideThe movement after the moment t is 0 when the boundary excitation does not change much, so as to
For example, as shown in FIG. 2. The actuator may make control decisions based on the prediction.
Taking the acceleration history in the X-axis direction of a three-axis stabilizer (the structure is shown in FIG. 4) as an example, when the a of the camera is predictedxAt t in the future1When a certain change course exists in the time period, the motion function relationship can be obtained according to curve fitting as follows:
ax(t)＝f(t)
in the formula: a isx(t) is the x-axis acceleration time history; and f (t) is a periodic motion function obtained by curve fitting.
And according to triaxial stabilizer hardware connection, can know:
ax＝g[θz,μy]
in the formula: a isxAcceleration in the x-axis, thetazIs the angle of rotation, mu, of the y-axis motoryThe projection of the distance between the center of mass of the camera and the axis of the z axis on the y axis is obtained.
From the mathematical geometry:
ax＝g[θz,μy]＝tanθz·μy
in order to compensate for the movement of the camera in the x-axis direction caused by the external periodic excitation, the movement in the x-axis direction needs to be offset by the rotation of the Z-axis motor on the basis of the prediction result, that is, the following steps are performed:
ax(t)＝-tanθz·μy
and finally obtaining a control output:
other freedom degree control is also given as the above steps, and finally the vector M of the prediction control result is obtained2Thereby realize the control to triaxial stabilizer:
in the formula: thetaxPredicting a rotation angle of the X-axis motor obtained by prediction control; thetayPredicting a rotation angle of the Y-axis motor obtained by prediction control; thetazAnd predicting the rotation angle of the Z-axis motor obtained by prediction control.
Further, in the above-mentioned case,
referring to fig. 3, since the external excitation is most likely to generate sudden change, the final control of the image capturing apparatus should be realized in combination with the real-time feedback control after obtaining the prediction control result.
In the actual control process, real-time control and prediction control are carried out synchronously, the real-time control, namely feedback control can carry out real-time feedback control through six-freedom-degree data, but certain hysteresis and errors exist, and the real-time control can obtain a real-time control result M1In the form of M2。
In particular, the method comprises the following steps of,
after a prediction control output matrix is obtained, namely a prediction control result, the prediction control output matrix is weighted and added with a real-time control result given by the real-time feedback control module, and the weighting addition is as follows:
ANS＝Wf1·M1+Wf2·M2
in the formula: wf1For real-time feedback of the control result matrix, Wf2For predicting the control result matrix, M1Weighting the coefficient matrix for real-time feedback control results, M2A coefficient matrix is weighted for the predictive control result.
Wherein the content of the first and second substances,
in the formula: mxFor the angle of rotation of the X-axis motor, MyFor the angle of rotation of the Y-axis motor, MzThe rotation angle of the Z-axis motor is required.
Wf1And Wf2The method is used for weighting and adding the real-time feedback control result and the prediction control result, and the following conditions are met:
Wf1+Wf2＝E
when the external excitation has obvious periodicity within a certain time, the weight of the prediction control result is increased during weighting, namely when the prediction result is accurate, W is increasedf2The ratio of (a) to (b).
When the external condition suddenly changes, namely the external excitation condition is not matched with the predicted value or the difference is large, namely:
P1-P2>δ
in the formula: p1For a predicted movement model obtained after BP curve fitting, P2For the actual motion model, δ is a redefinition threshold of the weighting coefficient matrix, which may be modified according to specific circumstances and requirements.
When the prediction result is large, i.e. P1-P2>δ weight W that should be given to the real-time feedback control result at the time of weightingf1Increasing or decreasing the weight W of the prediction control resultf2Even disabling predictive control; and the point acquisition module is instructed to read in the point data again to try to refit.
At initial run time, in the result weighting function, Wf1Should be a 1 coefficient matrix, Wf2A zero coefficient matrix should be used.
When the system starts to work, only the real-time feedback control module participates in control, and when the prediction control module detects that one or more freedom degree variables in external excitation have obvious rules, the prediction control module starts to work to obtain the weighted sum result of each freedom degree variable. And summing the obtained prediction result and the feedback control result through another weighting matrix to obtain a final control result.
After obtaining the final predicted result ANS:
and outputting the ANS to the motor driving module, wherein the motor driving controls X, Y, Z-axis motors to rotate through PWM modulation, namely, a rotation angle matrix is finally output to the motors.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (5)
1. An imaging stability control method based on predictive analysis, comprising the steps of:
step 1, continuously acquiring X, Y, Z three axial accelerations and angular velocities of the camera before a certain time point;
step 2, analyzing the motion of the camera before the certain time point according to the acquired acceleration and angular velocity in the three axial directions, and determining whether the camera is under the action of periodic external excitation before the certain time point; if yes, carrying out the next step; if not, performing the step 4;
step 2 comprises the following substeps:
step 2a, amplifying signals and filtering to remove noise of the acquired acceleration and angular velocity data in three axial directions;
substep 2b, taking a plurality of different time period lengths, respectively and equidistantly taking n time points in each time period length, and reading the n time points into a processor;
the plurality of different time periods is 3;
in the substep 2c, the processor periodically judges the data of each n time points in a plurality of time periods;
step 3, predicting the motion situation after a certain time point according to the motion periodicity of the camera shooting equipment before the certain time point, obtaining a prediction control result according to the prediction result, and controlling the camera shooting equipment;
step 4, acquiring a real-time control result and a prediction control result, combining the prediction control result and a real-time feedback control result to acquire a final control result, and controlling the camera equipment;
the combination of the prediction control result and the real-time feedback control result specifically comprises the following steps: and respectively establishing a prediction control result weighting coefficient matrix and a real-time feedback control result weighting coefficient matrix for carrying out weighted addition on the prediction control result and the real-time feedback control result.
2. The image stabilization control method based on predictive analysis according to claim 1, wherein step 3 includes the substeps of:
the substep 3a, establishing three weighting coefficient matrixes for carrying out weighted addition on the acceleration and angular velocity data acquired in three axial directions;
substep 3b, performing curve fitting on the three groups of data obtained after weighting to obtain time histories of each axial acceleration and angular velocity in three time periods before the certain time point;
and a substep 3c, predicting the motion situation of the camera equipment after the certain time point according to the change of the axial acceleration and the angular velocity in the three time periods, obtaining a prediction control output matrix, and controlling the camera equipment to keep stable.
3. The image stabilization control method based on predictive analysis according to claim 2, wherein in the sub-step 3a, the sum of three weighting coefficient matrices is a fixed matrix.
4. The image stabilization control method based on predictive analysis according to claim 2, wherein in sub-step 3b, curve fitting is BP curve fitting.
5. The camera stabilization control method based on predictive analysis according to claim 1, characterized in that X, Y, Z three axial accelerations and angular velocities of the camera device before a certain time point are continuously acquired by using a three-axis gyroscope.
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