CN104266636A - Method for resolving accelerated speed of health monitoring target point of building model based on high-speed video - Google Patents

Method for resolving accelerated speed of health monitoring target point of building model based on high-speed video Download PDF

Info

Publication number
CN104266636A
CN104266636A CN201410482638.3A CN201410482638A CN104266636A CN 104266636 A CN104266636 A CN 104266636A CN 201410482638 A CN201410482638 A CN 201410482638A CN 104266636 A CN104266636 A CN 104266636A
Authority
CN
China
Prior art keywords
impact point
speed
point
acceleration
phase place
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201410482638.3A
Other languages
Chinese (zh)
Other versions
CN104266636B (en
Inventor
刘祥磊
江涛
马静
庞蕾
张学东
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing University of Civil Engineering and Architecture
Original Assignee
Beijing University of Civil Engineering and Architecture
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing University of Civil Engineering and Architecture filed Critical Beijing University of Civil Engineering and Architecture
Priority to CN201410482638.3A priority Critical patent/CN104266636B/en
Publication of CN104266636A publication Critical patent/CN104266636A/en
Application granted granted Critical
Publication of CN104266636B publication Critical patent/CN104266636B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/36—Videogrammetry, i.e. electronic processing of video signals from a single source or from different sources to give parallax or range information
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
    • G01P15/02—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration by making use of inertia forces using solid seismic masses
    • G01P15/03—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration by making use of inertia forces using solid seismic masses by using non-electrical means

Abstract

The invention relates to the data processing field of building model health monitoring data based on high-speed video, in particular to a method for resolving the accelerated speed of a health monitoring target point of a building model based on high-speed video. The technical scheme is as follows: the method takes three-dimensional space coordinates of the target point, resolved by bundle adjustment, as a data source; the speed of the target point is calculated by a numerical differentiation method; the speed value of the target point, acquired by a seven-point Savitzky-Golay filter, is subjected to primary noise reduction treatment, the speed value of the target point, acquired through noise reduction treatment, is used as a data source, the accelerated speed of the target point is calculated through numerical differentiation; the accelerated speed of the target point, acquired by a nine-point Savitzky-Golay filter, is subjected to secondary noise reduction treatment to obtain high-precision target point accelerated speed. The method can reduce the influence of high-frequency noise on the resolving process of the accelerated speed of the building model health monitoring target point based on high-speed video, and can improve the resolving precision of the accelerated speed of the target point.

Description

A kind of high-speed video that is used for measures building model health monitoring impact point acceleration calculation method
Technical field
The present invention relates to high-speed video and measure building model health monitoring data processing field, particularly relate to a kind of for high-speed video measurement building model health monitoring impact point acceleration calculation method.
Background technology
In recent years, along with economic and social develops rapidly, many large-scale buildingss throughout world various places, the stability of building and shock resistance have more and more caused the concern of society, in order to ensure the security of buildings, needed before building construction according to certain ratio structure building model, and pass through shaking table, explosion, the External Force Actings such as shock carry out building model health monitoring experiment, obtain the dynamic response of building model key point to infer its failure mechanism, and then take to improve one's methods the accordingly stability and shock resistance that improve buildings, the expert of many field of civil engineering and slip-stick artist have carried out large quantifier elimination to this.At present, mainly adopt touch sensor for the technology catching and analyze building model dynamic response process, comprise displacement meter, accelerometer, clock gauge and linear variable displacement transducer etc.Touch sensor refers to that sensor must be fixed on and measures user specifies on building model position to obtain the multidate information of this position, and this feature determines it and has the shortcomings such as one-dimension, short range and destructible.
In order to overcome the shortcoming of touch sensor for building model health monitoring, in recent years, along with developing rapidly of sensor technology, the contactless video measuring based on high speed camera progressively launches application in building model health monitoring.Video measuring is one and is integrated with one that close-range photogrammetry and computer vision advantage separately grow up and calculates the contactless measurement of target three-dimensional coordinate as the image sequence function of time, can Dynamic Acquisition clarification of objective information to reach.High-speed video measurement has the advantage that noncontact, intensive measurement and quick three-dimensional measure, locus and the state of object can be recorded in a flash, quantitative test can be carried out in time to dynamic object, and using the whole generating process of dynamic object as dossier.In field of civil engineering, acceleration etc. are the important parameters describing building model health monitoring dynamic response process, it can reflect the dynamic response process of building model very intuitively, in detail, accurately, thus carries out rationally, accurately analyzing to the failure mechanism of building model.It is general higher that high-speed video measures camera frame frequency, and the acquisition time interval namely in image sequence in adjacent shots is shorter, inevitably there is the impact of high frequency noise, affects the precision that impact point acceleration resolves.
Summary of the invention
For above-mentioned technical matters, the present invention has designed and developed one and has measured building model health monitoring impact point acceleration calculation method for high-speed video, reduces the impact of high-speed video measuring process high frequency noise, improves the precision that impact point acceleration resolves.
Technical scheme provided by the invention is:
A kind of high-speed video that is used for measures building model health monitoring impact point acceleration calculation method, comprises the following steps:
The three dimensional space coordinate of step one, employing bundle adjustment model solution impact point;
Step 2, the current spatial location of a certain phase place of calculating image sequence and the range difference of this impact point initial phase locus obtain the displacement of impact point;
Step 3, with the shift value of impact point for data source, adopt the speed initial value of two point value differential calculation impact points, and adopt 7 Savitzky-Golay wave filters to obtain impact point speed initial value carry out first noise reduction process;
Step 4, with the velocity amplitude of the first noise reduction process of impact point for data source, adopt the acceleration initial value of two point value differential calculation impact points, and adopt 9 Savitzky-Golay wave filters to carry out secondary noise reduction process to the impact point acceleration initial value obtained, obtain high-precision impact point accekeration.
Preferably, described measures building model health monitoring impact point acceleration calculation method for high-speed video, bundle adjustment model in step one is considered as true value reference mark coordinate, the three dimensional space coordinate of impact point and the outer orientation parameter of camera are considered as unknown-value, combine and solve the object space coordinate of impact point and the outer orientation parameter of camera.The three dimensional space coordinate of impact point can be obtained by following formula:
V＝At+BX-L
Wherein, V is the error equation group listed by picture point; T is the column matrix be made up of image elements of exterior orientation, and A is the parameter matrix of matrix t; X is the column matrix of whole fixed point coordinate correction composition in model, and B is the parameter matrix of matrix X; L is the constant term of error equation.
Preferably, described measure building model health monitoring impact point acceleration calculation method for high-speed video, the displacement of the impact point in described step 2 refers to the range difference of impact point in the current spatial location of a certain phase place of image sequence and this impact point initial phase locus.The initial displacement of described impact point is set to 0mm, then impact point obtains by following formula at the shift value of the X of phase place n, Y and Z:
S X n = X n - X 1 S Y n = Y n - Y 1 S Z n = Z n - Z 1
Wherein, with represent the X of impact point at phase place n respectively, the shift value of Y and Z-direction; X 1, Y 1and Z 1represent the X of impact point at initial phase respectively, the coordinate figure of Y and Z-direction; X n, Y nand Z nrepresent the X of impact point at phase place n respectively, the coordinate figure of Y and Z-direction.
Preferably, described measures building model health monitoring impact point acceleration calculation method for high-speed video, impact point in described step 3 is the average velocity of impact point between phase place n-1 and phase place n+1 at the speed definition of phase place n, and described step comprises as follows:
A, with the displacement data of impact point for data source, adopt the speed initial value of 2 point value differential solving target points;
B, the speed initial value of impact point that obtains with step a, for data source, adopt 7 Savitzky-Golay wave filters to carry out first noise reduction process to the impact point speed initial value obtained, elimination high frequency noise.
Preferably, described measures building model health monitoring impact point acceleration calculation method for high-speed video, adopts the speed initial value of 2 point value differential solving target points to obtain by following formula in described step a:
V X n = ( X n + 1 - X n - 1 ) / 2 ΔT V Y n = ( Y n + 1 - Y n - 1 ) / 2 ΔT V Z n = ( Z n + 1 - Z n - 1 ) / 2 ΔT
Wherein, with represent the X of impact point at phase place n, the speed of Y and Z-direction; X n+1, Y n+1and Z n+1represent the X of impact point at phase place n+1, Y and Z-direction three dimensional space coordinate; X n-1, Y n-1and Z n-1represent the X of impact point at phase place n-1, Y and Z-direction three dimensional space coordinate; Δ T represents the time interval of image sequence adjacent phase.
Preferably, described measures building model health monitoring impact point acceleration calculation method for high-speed video, adopt 7 Savitzky-Golay wave filters to carry out first noise reduction process to the impact point speed initial value obtained in described step b, obtain by following formula:
V i ′ = 5 V i - 3 - 30 V i - 2 + 75 V i - 1 + 131 V i + 75 V i + 1 - 30 V i + 2 5 V i + 3 231
Wherein V ' ithe rate smoothing value of impact point i, V i-3v i+3continuous 7 velocity amplitudes obtained by numerical differentiation.
Preferably, described measures building model health monitoring impact point acceleration calculation method for high-speed video, and the acceleration of the impact point in described step 4 is defined as the average velocity of impact point at phase place n-1 and phase place n+1, and described step comprises as follows:
A, with the speed data of the impact point through 7 first noise reduction process of Savitzky-Golay wave filter for data source, adopt the acceleration initial value of 2 point value differential solving target points;
B, the acceleration initial value of impact point that obtains with step a, for data source, adopt 9 Savitzky-Golay wave filters to carry out secondary noise reduction process to the impact point acceleration initial value obtained, elimination high frequency noise.
Preferably, described measures building model health monitoring impact point acceleration calculation method for high-speed video, adopts the acceleration initial value of 2 point value differential solving target points to obtain by following formula in described step a:
a X n = ( V X n + 1 - V X n - 1 ) / 2 ΔT a Y n = ( V Y n + 1 - V Y n - 1 ) / 2 ΔT a Z n = ( V Z n + 1 - V Z n - 1 ) / ΔT
Wherein, with represent the X of impact point at phase place n, the acceleration of Y and Z-direction; with represent the X of impact point at phase place n+1, the speed of Y and Z-direction; with represent the X of impact point at phase place n-1, the speed of Y and Z-direction; Δ T represents the time interval of the adjacent image of image sequence.
Preferably, described measures building model health monitoring impact point acceleration calculation method for high-speed video, adopt 9 Savitzky-Golay wave filters to carry out secondary noise reduction process to the impact point acceleration initial value obtained in described step b, obtain by following formula:
a i ′ = 15 a i - 4 - 55 a i - 3 + 30 V i - 2 + 135 a i - 1 + 179 a i + 135 a i + 1 + 30 a i + 2 - 55 a i + 3 + 15 a i + 4 429
Wherein a ' ithe acceleration smooth value of impact point i, a i-4a i+4it is the acceleration initial value obtained by step a.
Of the present invention have following beneficial effect for high-speed video measurement building model health monitoring impact point acceleration calculation method: bundle adjustment has theoretical tight, the advantage that precision is high, the precision adopting bundle adjustment to resolve the three-dimensional coordinate of impact point in image sequence is better than the precision of 1:1 ten thousand, meets the demand that building model health monitoring impact point acceleration resolves; Two-step approach is adopted to eliminate the high frequency noise impact of acceleration solution process, namely the high frequency noise impact of two point value differential and 7 Savitzky-Golay wave filter elimination impact point velocity calculated processes is first adopted, then the high frequency noise impact of two point value differential and 9 Savitzky-Golay wave filter elimination impact point acceleration solution process is adopted, can not only farthest eliminate the high frequency noise produced in video measuring process, and the extreme value of speed and acceleration can be retained.Method of the present invention is the precondition that the building model failure mechanism carrying out measuring building model health monitoring based on high-speed video is analyzed, only has the acceleration information obtaining impact point accurately, Data support could be provided to the Dynamic Response of the building model of high-speed motion, and then the flight characteristic of building model or failure mechanism are studied.
Accompanying drawing explanation
Fig. 1 is the process flow diagram measuring building model health monitoring impact point acceleration calculation method for high-speed video of the present invention;
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in further detail, can implement according to this with reference to instructions word to make those skilled in the art.
As shown in Figure 1, the invention provides a kind of for high-speed video measurement building model health monitoring impact point acceleration calculation method, comprise the following steps:
Step one, bundle adjustment have theoretical tight, the advantage that precision is high.In bundle adjustment model, reference mark coordinate is considered as true value, the three dimensional space coordinate of impact point and the outer orientation parameter of camera are considered as unknown-value, combine and solve the object space coordinate of impact point and the outer orientation parameter of camera.The three dimensional space coordinate of impact point can be obtained by following formula:
V＝At+BX-L
Wherein, V is the error equation group listed by picture point; T is the column matrix be made up of image elements of exterior orientation, and A is the parameter matrix of matrix t; X is the column matrix of whole fixed point coordinate correction composition in model, and B is the parameter matrix of matrix X; L is the constant term of error equation.
The displacement of step 2, impact point is the basic data source resolving acceleration.The displacement of impact point refers to the range difference of impact point in the current spatial location of a certain phase place of image sequence and this impact point initial phase locus.The initial displacement of described impact point is set to 0mm, then impact point obtains by following formula at the shift value of the X of phase place n, Y and Z-direction:
S X n = X n - X 1 S Y n = Y n - Y 1 S Z n = Z n - Z 1
Wherein, with represent the X of impact point at phase place n respectively, the shift value of Y and Z-direction; X 1, Y 1and Z 1represent the coordinate figure of impact point at X, Y and Z-direction initial phase respectively; X n, Y nand Z nrepresent the coordinate figure of impact point at X, Y and Z-direction phase place n respectively.
Step 3, speed are the physical quantitys describing particle movement speed and direction, equal displacementwith the ratio that this displacement time used occurs.In the bright method of we, impact point is the average velocity of impact point between phase place n-1 and phase place n+1 at the speed definition of phase place n.Numerical differentiation has the effect eliminating inherent noise.Savitzky-Golay wave filter be a kind of in time domain based on polynomial expression, and utilize least square method to carry out the method for best-fit by moving window.The obvious purposes of Savitzky-Golay wave filter is smooth noise data, and data smoothing can be eliminated all with the data point compared with big error obstacle, or from figure, make preliminary and coarse simple parameter estimation.The wave filter difference of Savitzky-Golay wave filter and other types is that it directly processes from data smoothing problem in time domain, and be transformed into time domain again after not needing first defined property in a frequency domain, the undistorted of raw data can be ensured like this, and more can retain relative maximum, minimal value and width equal distribution characteristic.The speed of impact point realizes in two steps, first with the displacement data of impact point for data source, adopt the speed initial value of 2 point value differential solving target points; Then with the speed initial value of impact point for data source, adopt 7 Savitzky-Golay wave filters to obtain impact point speed initial value carry out first noise reduction process, eliminate high frequency noise.
Step 4, impact point acceleration are velocity variable and the ratio that this change time used occurs, and are to describe the physical quantity that object speed changes speed.In the methods of the invention, the acceleration of impact point is defined as the average velocity of impact point at phase place n-1 and phase place n+1, first with the speed data of the impact point through 7 Savitzky-Golay wave filter noise reduction process for data source, adopt the acceleration initial value of 2 point value differential solving target points; Then with obtain impact point acceleration initial value for data source, adopts 9 Savitzky-Golay wave filters to acquisition impact point acceleration initial value carry out secondary noise reduction process, eliminate high frequency noise further.
Described one is used for high-speed video and measures in building model health monitoring impact point acceleration calculation method, specifically comprises in described step 3:
A. with the displacement data of impact point for data source, adopt the speed initial value of 2 point value differential solving target points, obtain by following formula:
V X n = ( X n + 1 - X n - 1 ) / 2 ΔT V Y n = ( Y n + 1 - Y n - 1 ) / 2 ΔT V Z n = ( Z n + 1 - Z n - 1 ) / 2 ΔT
Wherein, with represent the X of impact point at phase place n, the speed of Y and Z-direction; X n+1, Y n+1and Z n+1represent the X of impact point at phase place n+1, Y and Z-direction three dimensional space coordinate; X n-1, Y n-1and Z n-1represent the X of impact point at phase place n-1, Y and Z-direction three dimensional space coordinate; Δ T represents the time interval of image sequence adjacent phase.
B. the speed initial value of impact point obtained with step a is for data source, and adopts 7 Savitzky-Golay wave filters to carry out first noise reduction process to the impact point speed initial value obtained, elimination high frequency noise, obtains by following formula:
V i ′ = 5 V i - 3 - 30 V i - 2 + 75 V i - 1 + 131 V i + 75 V i + 1 - 30 V i + 2 5 V i + 3 231
Wherein V ' ithe rate smoothing value of impact point i, V i-3v i+3continuous 7 velocity amplitudes obtained by numerical differentiation.
Described one is used for high-speed video and measures in building model health monitoring impact point acceleration calculation method, specifically comprises in described step 4:
A. with the speed data of the impact point through 7 Savitzky-Golay wave filter noise reduction process for data source, adopt the acceleration initial value of 2 point value differential solving target points, obtain by following formula:
a X n = ( V X n + 1 - V X n - 1 ) / 2 ΔT a Y n = ( V Y n + 1 - V Y n - 1 ) / 2 ΔT a Z n = ( V Z n + 1 - V Z n - 1 ) / ΔT
Wherein, with represent the acceleration of impact point at phase place n; with represent the X of impact point at phase place n+1, the speed of Y and Z-direction; with represent the X of impact point at phase place n-1, the speed of Y and Z-direction; Δ T represents the time interval of the adjacent image of image sequence.
B. the acceleration initial value of impact point obtained with step a, for data source, adopts the impact point acceleration initial value of 9 Savitzky-Golay wave filters to acquisition to carry out secondary noise reduction process, eliminates high frequency noise further, obtain by following formula:
a i ′ = 15 a i - 4 - 55 a i - 3 + 30 V i - 2 + 135 a i - 1 + 179 a i + 135 a i + 1 + 30 a i + 2 - 55 a i + 3 + 15 a i + 4 429
Wherein a ' ithe acceleration smooth value of impact point i, a i-4a i+4it is the acceleration initial value obtained by step a.
Although embodiment of the present invention are open as above, but it is not restricted to listed in instructions and embodiment utilization, it can be applied to various applicable the field of the invention completely, for those skilled in the art, can easily realize other amendment, therefore do not deviating under the universal that claim and equivalency range limit, the present invention is not limited to specific details and illustrates here and the legend described.

Claims (9)

1. measure a building model health monitoring impact point acceleration calculation method for high-speed video, it is characterized in that, comprise the following steps:
The three dimensional space coordinate of step one, employing bundle adjustment model solution impact point;
Step 2, the current spatial location of a certain phase place of calculating image sequence and the range difference of this impact point initial phase locus obtain the displacement of impact point;
Step 3, with the shift value of impact point for data source, adopt the speed initial value of two point value differential calculation impact points, and adopt 7 Savitzky-Golay wave filters to obtain impact point speed initial value carry out first noise reduction process;
Step 4, with the velocity amplitude of the first noise reduction process of impact point for data source, adopt the acceleration initial value of two point value differential calculation impact points, and adopt 9 Savitzky-Golay wave filters to carry out secondary noise reduction process to the impact point acceleration initial value obtained, obtain high-precision impact point accekeration.
2. measure building model health monitoring impact point acceleration calculation method for high-speed video as claimed in claim 1, it is characterized in that, bundle adjustment model in described step one is considered as true value reference mark coordinate, the three dimensional space coordinate of impact point and the outer orientation parameter of camera are considered as unknown-value, combine and solve the object space coordinate of impact point and the outer orientation parameter of camera.The three dimensional space coordinate of impact point can be obtained by following formula:
V＝At+BX-L
Wherein, V is the error equation group listed by picture point; T is the column matrix be made up of image elements of exterior orientation, and A is the parameter matrix of matrix t; X is the column matrix of whole fixed point coordinate correction composition in model, and B is the parameter matrix of matrix X; L is the constant term of error equation.
3. measure building model health monitoring impact point acceleration calculation method for high-speed video as claimed in claim 1, it is characterized in that, the displacement of the impact point in described step 2 refers to the range difference of impact point in the current spatial location of a certain phase place of image sequence and this impact point initial phase locus.The initial displacement of described impact point is set to 0mm, then impact point obtains by following formula at the shift value of the X of phase place n, Y and Z-direction:
S X n = X n - X 1 S Y n = Y n - Y 1 S Z n = Z n - Z 1
Wherein, with represent the X of impact point at phase place n respectively, the shift value of Y and Z-direction; X 1, Y 1and Z 1represent the X of impact point at initial phase respectively, the coordinate figure of Y and Z-direction; X n, Y nand Z nrepresent the X of impact point at phase place n respectively, the coordinate figure of Y and Z-direction.
4. measure building model health monitoring impact point acceleration calculation method for high-speed video as claimed in claim 1, it is characterized in that, impact point in described step 3 is the average velocity of impact point between phase place n-1 and phase place n+1 at the speed definition of phase place n, and described step comprises as follows:
A, with the displacement data of impact point for data source, adopt the speed initial value of 2 point value differential solving target points;
B, the speed initial value of impact point that obtains with step a, for data source, adopt 7 Savitzky-Golay wave filters to carry out first noise reduction process to the impact point speed initial value obtained, elimination high frequency noise.
5. measure building model health monitoring impact point acceleration calculation method for high-speed video as claimed in claim 4, it is characterized in that, in described step a, adopt the speed initial value of 2 point value differential solving target points to obtain by following formula:
V X n = ( X n + 1 - X n - 1 ) / 2 ΔT V Y n = ( Y n + 1 - Y n - 1 ) / 2 ΔT V Z n = ( Z n + 1 - Z n - 1 ) / 2 ΔT
Wherein, with represent the X of impact point at phase place n, the speed of Y and Z-direction; X n+1, Y n+1and Z n+1represent the X of impact point at phase place n+1, Y and Z-direction three dimensional space coordinate; X n-1, Y n-1and Z n-1represent the X of impact point at phase place n-1, Y and Z-direction three dimensional space coordinate; Δ T represents the time interval of image sequence adjacent phase.
6. measure building model health monitoring impact point acceleration calculation method for high-speed video as claimed in claim 4, it is characterized in that, adopt 7 Savitzky-Golay wave filters to carry out first noise reduction process to the impact point speed initial value obtained in described step b, obtain by following formula:
V i ′ = 5 V i - 3 - 30 V i - 2 + 75 V i - 1 + 131 V i + 75 V i + 1 - 30 V i + 2 5 V i + 3 231
Wherein V ' ithe rate smoothing value of impact point i, V i-3v i+3continuous 7 velocity amplitudes obtained by numerical differentiation.
7. measure building model health monitoring impact point acceleration calculation method for high-speed video as claimed in claim 1, it is characterized in that, the acceleration of the impact point in described step 4 is defined as the average velocity of impact point at phase place n-1 and phase place n+1, and described step comprises as follows:
A, with the speed data of the impact point through 7 first noise reduction process of Savitzky-Golay wave filter for data source, adopt the acceleration initial value of 2 point value differential solving target points;
B, the acceleration initial value of impact point that obtains with step a, for data source, adopt 9 Savitzky-Golay wave filters to carry out secondary noise reduction process to the impact point acceleration initial value obtained, elimination high frequency noise.
8. measure building model health monitoring impact point acceleration calculation method for high-speed video as claimed in claim 7, it is characterized in that, in described step a, adopt the acceleration initial value of 2 point value differential solving target points to obtain by following formula:
a X n = ( V X n + 1 - V X n - 1 ) / 2 ΔT a Y n = ( V Y n + 1 - V Y n - 1 ) / 2 ΔT a Z n = ( V Z n + 1 - V Z n - 1 ) / ΔT
Wherein, with represent the X of impact point at phase place n, the acceleration of Y and Z-direction; with represent the X of impact point at phase place n+1, the speed of Y and Z-direction; with represent the X of impact point at phase place n-1, the speed of Y and Z-direction; Δ T represents the time interval of the adjacent image of image sequence.
9. measure building model health monitoring impact point acceleration calculation method for high-speed video as claimed in claim 7, it is characterized in that, adopt 9 Savitzky-Golay wave filters to carry out secondary noise reduction process to the impact point acceleration initial value obtained in described step b, obtain by following formula:
a i ′ = 15 a i - 4 - 55 a i - 3 + 30 V i - 2 + 135 a i - 1 + 179 a i + 135 a i + 1 + 30 a i + 2 - 55 a i + 3 + 15 a i + 4 429
Wherein a ' ithe acceleration smooth value of impact point i, a i-4a i+4it is the acceleration initial value obtained by step a.
CN201410482638.3A 2014-09-19 2014-09-19 Method for resolving accelerated speed of health monitoring target point of building model based on high-speed video Expired - Fee Related CN104266636B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201410482638.3A CN104266636B (en) 2014-09-19 2014-09-19 Method for resolving accelerated speed of health monitoring target point of building model based on high-speed video

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201410482638.3A CN104266636B (en) 2014-09-19 2014-09-19 Method for resolving accelerated speed of health monitoring target point of building model based on high-speed video

Publications (2)

Publication Number Publication Date
CN104266636A true CN104266636A (en) 2015-01-07
CN104266636B CN104266636B (en) 2017-04-19

Family

ID=52158177

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201410482638.3A Expired - Fee Related CN104266636B (en) 2014-09-19 2014-09-19 Method for resolving accelerated speed of health monitoring target point of building model based on high-speed video

Country Status (1)

Country Link
CN (1) CN104266636B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107421509A (en) * 2017-07-10 2017-12-01 同济大学 A kind of high-speed video measuring method of reticulated shell type Approaches for Progressive Collapse of Structures
CN114993606A (en) * 2022-05-31 2022-09-02 中国科学院力学研究所 Wind tunnel test result processing method for unsteady pressure and aerodynamic data

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101871862A (en) * 2010-06-18 2010-10-27 北京工业大学 Novel method for recognizing rigidity between shearing type structure layers under environmental excitation
CN102128725A (en) * 2010-12-02 2011-07-20 李惠 Method for monitoring health and pre-warning safety of large-span space structure
CN102538742A (en) * 2012-01-09 2012-07-04 中国矿业大学 Deformation measurement and early warning system and method integrating satellite positioning and accelerometer
CN102937646A (en) * 2012-11-08 2013-02-20 沈阳建筑大学 Health monitoring system for concrete structure

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101871862A (en) * 2010-06-18 2010-10-27 北京工业大学 Novel method for recognizing rigidity between shearing type structure layers under environmental excitation
CN102128725A (en) * 2010-12-02 2011-07-20 李惠 Method for monitoring health and pre-warning safety of large-span space structure
CN102538742A (en) * 2012-01-09 2012-07-04 中国矿业大学 Deformation measurement and early warning system and method integrating satellite positioning and accelerometer
CN102937646A (en) * 2012-11-08 2013-02-20 沈阳建筑大学 Health monitoring system for concrete structure

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107421509A (en) * 2017-07-10 2017-12-01 同济大学 A kind of high-speed video measuring method of reticulated shell type Approaches for Progressive Collapse of Structures
CN107421509B (en) * 2017-07-10 2019-08-02 同济大学 A kind of high-speed video measurement method of reticulated shell type Approaches for Progressive Collapse of Structures
CN114993606A (en) * 2022-05-31 2022-09-02 中国科学院力学研究所 Wind tunnel test result processing method for unsteady pressure and aerodynamic data

Also Published As

Publication number Publication date
CN104266636B (en) 2017-04-19

Similar Documents

Publication Publication Date Title
Tian et al. Pedestrian dead reckoning for MARG navigation using a smartphone
CN110702122B (en) Comprehensive optimization method for autonomous optical navigation characteristics of extraterrestrial celestial body landing
CN103913171A (en) Compensation measurement method for rotary speed and rotary angle of carrier based on accelerometer and gyroscope
CN105404296A (en) Two-wheeled self-balancing intelligent vehicle posture control method
CN105043348A (en) Accelerometer gyroscope horizontal angle measurement method based on Kalman filtering
Sun et al. Adaptive sensor data fusion in motion capture
CN102221341A (en) Quick digital image correlation measurement method based on stochastic parallel gradient descent optimization technology
CN104880172B (en) Measuring flatness of road surface method and device based on Kalman filtering
CN107741240A (en) A kind of combined inertial nevigation system self-adaption Initial Alignment Method suitable for communication in moving
CN107782309A (en) Noninertial system vision and double tops instrument multi tate CKF fusion attitude measurement methods
Deconinck et al. Relating the bottom pressure and the surface elevation in the water wave problem
CN103400393B (en) A kind of image matching method and system
CN108307174A (en) A kind of depth image sensor precision improvement method and system
CN104266636A (en) Method for resolving accelerated speed of health monitoring target point of building model based on high-speed video
CN103913114B (en) Method for position association between binocular vision systems
Murray et al. Dynamic measurements using digital image correlation
CN104075710B (en) A kind of motor-driven Extended target based on Trajectory Prediction axial attitude real-time estimation method
CN110672127B (en) Real-time calibration method for array type MEMS magnetic sensor
CN102798380B (en) Method for measuring motion parameters of target in linear array image
CN103822768B (en) A kind of ultra-low-frequency horizontal is to the static equalising means of shaking table guide rail irregularity
Iwai et al. Development of a measuring method for motion accuracy of NC machine tools using links and rotary encoders
CN107621261A (en) The adaptive optimal REQUEST algorithms resolved for inertia earth magnetism assembled gesture
CN107340026A (en) Unstable state level gauging value filtering method
CN101598540B (en) Three-dimensional positioning method and three-dimensional positioning system
CN103616024A (en) Method for determining observability of planetary exploration entry section autonomous navigation system

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20170419

Termination date: 20170919