CN103456016A - Body-feeling camera network calibration method unrelated to visual angles - Google Patents
Body-feeling camera network calibration method unrelated to visual angles Download PDFInfo
- Publication number
- CN103456016A CN103456016A CN2013104039881A CN201310403988A CN103456016A CN 103456016 A CN103456016 A CN 103456016A CN 2013104039881 A CN2013104039881 A CN 2013104039881A CN 201310403988 A CN201310403988 A CN 201310403988A CN 103456016 A CN103456016 A CN 103456016A
- Authority
- CN
- China
- Prior art keywords
- coordinate system
- video camera
- human skeleton
- rotation matrix
- frame
- 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
Links
Images
Landscapes
- Image Analysis (AREA)
- Image Processing (AREA)
- Closed-Circuit Television Systems (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention relates to a body-feeling camera network calibration method unrelated to visual angles. The method comprises the steps that 1) the coordinate system of one of a plurality of cameras which are installed is selected to serve as a reference coordinate system, and other coordinate systems are non-reference coordinate systems; 2) the cameras obtain and display skeleton position information of the same person simultaneously; 3) a human skeleton coordinate system is built, and the cameras calculate rotation matrixes and horizontally-moving vectors of the cameras relative to the human skeleton coordinate system respectively according to the skeleton position information obtained in a detected mode; 4) according to the rotation matrixes and the horizontally-moving vectors obtained in the step 3), rotation matrixes and horizontally-moving vectors of all the non-reference coordinate systems relative to the reference coordinate system are calculated; 5) according to the rotation matrixes and the horizontally-moving vectors obtained in the step 4), the skeleton position information detected the cameras in the non-reference coordinate systems is mapped in the reference coordinate system. Compared with the prior art, the body-feeling camera network calibration method has the advantages that operation is simple, practicality is strong, and other auxiliary markers do not need to be prepared specially.
Description
Technical field
The present invention relates to a kind of camera marking method, the body sense video camera network mark that especially relates to a kind of view angle-independent is determined method.
Background technology
The demarcation of camera can be divided into two classes: a class is the demarcation of one camera; Another kind of is the global calibration of polyphaser.The demarcation of one camera refers to the Relation Parameters solved between every camera inside and outside parameter and camera.Method commonly used is the chessboard calibration method of Zhengyou Zhang proposition and two standardizations of RAC that Tsai proposes.Wherein, the chessboard calibration method that Zhang proposes, serve as a mark a little by the summit by artificial mark chessboard black and white chess lattice, and stated accuracy is high, has become a kind of method of main flow.The global calibration of polyphaser refers to the data unification of all cameras in the overall frame of reference, obtains the relative coordinate between camera, namely translation vector T and the rotation matrix R between camera coordinates system.Main thought is to find out the 3D gauge point jointly detected between camera to calculate the spatial relationship between camera.The point cloud that adopts kinect to detect at present is gauge point, by the ICP method, calculates.The major defect of said method is that calculated amount is large, needs extra auxiliary reference thing.
Summary of the invention
Purpose of the present invention is exactly in order to overcome the defect that above-mentioned prior art exists, to provide a kind of body sense video camera network mark of simple to operate, real-time view angle-independent to determine method.
Purpose of the present invention can be achieved through the following technical solutions:
A kind of body sense video camera network mark of view angle-independent is determined method, and the method comprises the following steps:
1) coordinate at the selected video camera place of the multiple cameras of installing is the frame of reference, and all the other are the non-frame of reference;
2) multiple cameras obtains the frame position information of same person simultaneously and shows;
3) set up the human skeleton coordinate system, multiple cameras respectively according to detect frame position information calculate rotation matrix and the translation vector of the relative human skeleton coordinate system of each video camera;
4) according to step 3) rotation matrix and the translation vector that obtain, calculate rotation matrix and the translation vector of each non-frame of reference relative datum coordinate system;
5) according to step 4) rotation matrix and the translation vector that obtain, the frame position information of non-frame of reference place video camera detecting is mapped in the frame of reference, and shows, complete demarcation.
Described step 3), in, the positional information of the left shoulder of the human body of take, right shoulder and barycenter is fundamental construction human skeleton coordinate system, is specially:
The left shoulder of take is X-direction to the direction of right shoulder, and the barycenter of take is Y direction to the central point of right and left shoulders, by the right-hand rule, determines Z-direction.
Described step 3), in, the peaceful amount of shifting to of rotation matrix of calculating the relative human skeleton coordinate system of each video camera is specially:
Obtain the left shoulder of skeleton, right shoulder, barycenter vector and the vector in the human skeleton coordinate system thereof in the coordinate system of video camera S place, calculate the rotation matrix of the relative human skeleton coordinate system of video camera S according to following formula
and translation vector
sp is the expression vector of some P correspondence in the coordinate system of video camera S place,
the peoplethe expression vector that P is same point P correspondence in the human skeleton coordinate system,
the rotation matrix that means the relative video camera S of human skeleton coordinate system place coordinate system,
the translation vector that means the relative video camera S of human skeleton coordinate system place coordinate system.
Described step 4), in, the peaceful amount of shifting to of rotation matrix of calculating each non-frame of reference relative datum coordinate system is specially:
The expression vector of some P correspondence in the human skeleton coordinate system
the peoplep is for being expressed as:
basep is the expression vector of some P correspondence in the coordinate system of fiducial cameras place,
the rotation matrix that means the relative human skeleton coordinate system of fiducial cameras,
mean the translation vector of fiducial cameras with respect to the human skeleton coordinate system,
non-p is the expression vector of some P correspondence in the coordinate system of non-fiducial cameras place,
the rotation matrix that means the relative human skeleton coordinate system of non-fiducial cameras,
mean the translation vector of non-fiducial cameras with respect to the human skeleton coordinate system,
The rotation matrix of non-frame of reference relative datum coordinate system
the translation vector of non-frame of reference relative datum coordinate system
Compared with prior art, it is that reference point builds the coordinate system for the view angle-independent of human skeleton that the present invention adopts the stable articulation point of human body, aid mark thing simple to operate, real-time, as not need specially to prepare other, and with the view angle-independent of each video camera.
The accompanying drawing explanation
Fig. 1 is schematic flow sheet of the present invention;
Fig. 2 is the human skeleton figure that depth camera detects;
Fig. 3 is for take the human skeleton coordinate system schematic diagram that sane articulation point is fundamental construction.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.The present embodiment be take technical solution of the present invention and is implemented as prerequisite, provided detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
Embodiment
As shown in Figure 1, a kind of body sense video camera network mark of view angle-independent is determined method, and the sane nominal volume sense video camera net fast of take is target, and sane skeleton joint point is reference point to propose to take human body, build the human skeleton coordinate system of view angle-independent, and adopt the method based on the coordinate system conversion to realize.The method comprises the following steps:
In step S101, the coordinate at the selected video camera place of the multiple cameras of installing is the frame of reference, and all the other are the non-frame of reference, and the present embodiment be take 2 as example, is assumed to A and B, and the coordinate at selected video camera A place is the frame of reference;
In step S102,2 video cameras obtain the frame position information of same person simultaneously and show;
In step S103, set up the human skeleton coordinate system, 2 video cameras respectively according to detect frame position information calculate the rotation matrix of the relative human skeleton coordinate system of each video camera
and translation vector
In step S104, according to rotation matrix and the translation vector of step S103 acquisition, calculate rotation matrix and the translation vector of each non-frame of reference relative datum coordinate system;
In step S105, the rotation matrix and the translation vector that according to step S104, obtain, the frame position information of non-frame of reference place video camera (referring to B here) detecting is mapped in the frame of reference (referring to A here), and shows, complete demarcation.
Take the view angle-independent switch technology that shoulder bone and barycenter be benchmark:
Depth camera kinect, under the assistance of software OpenNI, can effectively obtain 3D coordinate and the direction of 15 articulation points of human body, and articulation point is shown in Fig. 2.By long observation and experiment, find, in 15 articulation points obtaining, stable articulation point mainly contains 4, is respectively left shoulder, right shoulder, head and barycenter.Simultaneously, the upper part of the body torso portion of also finding human body is all very stable, is difficult to change, and the result that kinect detects is also the same.So the present invention proposes to take left shoulder, right shoulder and barycenter as fundamental construction human skeleton coordinate system.The left shoulder of take is X-direction to the direction of right shoulder, and the barycenter of take is Y-direction to the central point of right and left shoulders, by the right-hand rule, determines the Z direction, specifically sees Fig. 3.
To calculate the rotation matrix of the relative human skeleton coordinate system of video camera A
and translation vector
for example,
Wherein,
athe expression vector of the correspondence in the coordinate system of camera A place of 1 P in the P representation space,
the peoplep means the expression vector of same point P correspondence in the human skeleton coordinate system,
the rotation matrix that means the relative camera A of human skeleton coordinate system,
mean that the human skeleton coordinate system is with respect to the translation vector at camera A, by measuring, the implication of whole equation expression is that the point in the human skeleton coordinate system is videoed in the coordinate system of camera A place.
The translation vector on formula (1) the right is moved to left, can obtain formula (2):
If by right shoulder at the positional representation of human skeleton coordinate system be: (t, 0,0), can obtain:
If by barycenter at the positional representation of human skeleton coordinate system be (0 ,-s, 0):
By the right-hand rule, can obtain
the 3rd row be just in time the 1st row and the 2nd apposition be listed as:
Due to, what need at present is that current coordinate system is mapped to the rotation matrix in the human skeleton coordinate system, what need is
(rotation matrix that means the relative human skeleton coordinate system of camera A) can be obtained by the characteristics of rotation matrix:
What in like manner need here to solve is with the human skeleton coordinate, to be the translation vector of benchmark
by formula (1), can be obtained
and due to
therefore:
In like manner can obtain the rotation matrix of the relative human skeleton coordinate system of video camera B
and translation vector
Conversion between coordinate system:
The point that the non-reference coordinate of 2 video cameras is detected is mapped in reference coordinate.In previous step, solved rotation matrix and the translation vector of relative human skeleton coordinate system, the purpose of this step is rotation matrix and the translation vector that solves the relative video camera A of video camera B.Basic ideas are to be that bridge is obtained with the human skeleton coordinate.
Suppose the coordinate system that three coordinate system video camera A are arranged now, the coordinate system of video camera B and the coordinate system of human skeleton.。By video camera A, to people's coordinate system conversion table, be shown:
By video camera B, to people's coordinate system conversion table, be shown:
Claims (4)
1. the body sense video camera network mark of a view angle-independent is determined method, it is characterized in that, the method comprises the following steps:
1) coordinate at the selected video camera place of the multiple cameras of installing is the frame of reference, and all the other are the non-frame of reference;
2) multiple cameras obtains the frame position information of same person simultaneously and shows;
3) set up the human skeleton coordinate system, multiple cameras respectively according to detect frame position information calculate rotation matrix and the translation vector of the relative human skeleton coordinate system of each video camera;
4) according to step 3) rotation matrix and the translation vector that obtain, calculate rotation matrix and the translation vector of each non-frame of reference relative datum coordinate system;
5) according to step 4) rotation matrix and the translation vector that obtain, the frame position information of non-frame of reference place video camera detecting is mapped in the frame of reference, and shows, complete demarcation.
2. the body sense video camera network mark of a kind of view angle-independent according to claim 1 is determined method, it is characterized in that described step 3) in, the positional information of the left shoulder of the human body of take, right shoulder and barycenter is fundamental construction human skeleton coordinate system, is specially:
The left shoulder of take is X-direction to the direction of right shoulder, and the barycenter of take is Y direction to the central point of right and left shoulders, by the right-hand rule, determines Z-direction.
3. the body sense video camera network mark of a kind of view angle-independent according to claim 2 is determined method, it is characterized in that described step 3) in, the peaceful amount of shifting to of rotation matrix of calculating the relative human skeleton coordinate system of each video camera is specially:
Obtain the left shoulder of skeleton, right shoulder, barycenter vector and the vector in the human skeleton coordinate system thereof in the coordinate system of video camera S place, calculate the rotation matrix of the relative human skeleton coordinate system of video camera S according to following formula
and translation vector
sp is the expression vector of some P correspondence in the coordinate system of video camera S place,
the peoplethe expression vector that P is same point P correspondence in the human skeleton coordinate system,
the rotation matrix that means the relative video camera S of human skeleton coordinate system place coordinate system,
the translation vector that means the relative video camera S of human skeleton coordinate system place coordinate system.
4. the body sense video camera network mark of a kind of view angle-independent according to claim 3 is determined method, it is characterized in that described step 4) in, the peaceful amount of shifting to of rotation matrix of calculating each non-frame of reference relative datum coordinate system is specially:
The expression vector of some P correspondence in the human skeleton coordinate system
the peoplep is for being expressed as:
basep is the expression vector of some P correspondence in the coordinate system of fiducial cameras place,
the rotation matrix that means the relative human skeleton coordinate system of fiducial cameras,
mean the translation vector of fiducial cameras with respect to the human skeleton coordinate system,
non-p is the expression vector of some P correspondence in the coordinate system of non-fiducial cameras place,
the rotation matrix that means the relative human skeleton coordinate system of non-fiducial cameras,
mean the translation vector of non-fiducial cameras with respect to the human skeleton coordinate system,
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310403988.1A CN103456016B (en) | 2013-09-06 | 2013-09-06 | The body-sensing video camera net scaling method that a kind of visual angle is unrelated |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310403988.1A CN103456016B (en) | 2013-09-06 | 2013-09-06 | The body-sensing video camera net scaling method that a kind of visual angle is unrelated |
Publications (2)
Publication Number | Publication Date |
---|---|
CN103456016A true CN103456016A (en) | 2013-12-18 |
CN103456016B CN103456016B (en) | 2016-07-13 |
Family
ID=49738344
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201310403988.1A Active CN103456016B (en) | 2013-09-06 | 2013-09-06 | The body-sensing video camera net scaling method that a kind of visual angle is unrelated |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN103456016B (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106060524A (en) * | 2016-06-30 | 2016-10-26 | 北京邮电大学 | Method and device for setting camera |
CN109241841A (en) * | 2018-08-01 | 2019-01-18 | 甘肃未来云数据科技有限公司 | The acquisition methods and device of video human movement |
CN113077519A (en) * | 2021-03-18 | 2021-07-06 | 中国电子科技集团公司第五十四研究所 | Multi-phase external parameter automatic calibration method based on human skeleton extraction |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101186038A (en) * | 2007-12-07 | 2008-05-28 | 北京航空航天大学 | Method for demarcating robot stretching hand and eye |
CN102622766A (en) * | 2012-03-01 | 2012-08-01 | 西安电子科技大学 | Multi-objective optimization multi-lens human motion tracking method |
CN102638653A (en) * | 2012-03-01 | 2012-08-15 | 北京航空航天大学 | Automatic face tracing method on basis of Kinect |
-
2013
- 2013-09-06 CN CN201310403988.1A patent/CN103456016B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101186038A (en) * | 2007-12-07 | 2008-05-28 | 北京航空航天大学 | Method for demarcating robot stretching hand and eye |
CN102622766A (en) * | 2012-03-01 | 2012-08-01 | 西安电子科技大学 | Multi-objective optimization multi-lens human motion tracking method |
CN102638653A (en) * | 2012-03-01 | 2012-08-15 | 北京航空航天大学 | Automatic face tracing method on basis of Kinect |
Non-Patent Citations (2)
Title |
---|
贾倩倩 等: "基于特征点图像序列的多摄像机全局标定", 《清华大学学报(自然科学版)》 * |
韩云 等: "以人体骨架为基础的室内实时动作侦测", 《第三十二届中国控制会议论文集》 * |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106060524A (en) * | 2016-06-30 | 2016-10-26 | 北京邮电大学 | Method and device for setting camera |
CN106060524B (en) * | 2016-06-30 | 2017-12-29 | 北京邮电大学 | The method to set up and device of a kind of video camera |
CN109241841A (en) * | 2018-08-01 | 2019-01-18 | 甘肃未来云数据科技有限公司 | The acquisition methods and device of video human movement |
CN109241841B (en) * | 2018-08-01 | 2022-07-05 | 甘肃未来云数据科技有限公司 | Method and device for acquiring video human body actions |
CN113077519A (en) * | 2021-03-18 | 2021-07-06 | 中国电子科技集团公司第五十四研究所 | Multi-phase external parameter automatic calibration method based on human skeleton extraction |
CN113077519B (en) * | 2021-03-18 | 2022-12-09 | 中国电子科技集团公司第五十四研究所 | Multi-phase external parameter automatic calibration method based on human skeleton extraction |
Also Published As
Publication number | Publication date |
---|---|
CN103456016B (en) | 2016-07-13 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN102155923B (en) | Splicing measuring method and system based on three-dimensional target | |
CN100429476C (en) | Double-sensor laser visual measuring system calibrating method | |
CN104075688B (en) | A kind of binocular solid stares the distance-finding method of monitoring system | |
CN105073348B (en) | Robot system and method for calibration | |
CN109242915A (en) | Multicamera system scaling method based on multi-face solid target | |
CN102980528B (en) | Calibration method of pose position-free constraint line laser monocular vision three-dimensional measurement sensor parameters | |
CN107883870A (en) | Overall calibration method based on binocular vision system and laser tracker measuring system | |
CN104864807B (en) | A kind of manipulator hand and eye calibrating method based on active binocular vision | |
CN107256568B (en) | High-precision mechanical arm hand-eye camera calibration method and calibration system | |
CN103033132B (en) | Plane survey method and device based on monocular vision | |
CN105205824A (en) | Multi-camera global calibration method based on high-precision auxiliary cameras and ball targets | |
CN103353388B (en) | A kind of binocular body formula micro imaging system scaling method of tool camera function and device | |
CN103258329B (en) | A kind of camera marking method based on ball one-dimensional | |
CN1971206A (en) | Calibration method for binocular vision sensor based on one-dimension target | |
CN108269286A (en) | Polyphaser pose correlating method based on combination dimensional mark | |
CN102930548B (en) | Utilize the intersecting elliptical linear solution camera intrinsic parameter that two identical | |
CN104111071B (en) | High-precision position posture calculating method based on laser ranging and camera visual fusion | |
CN101750014A (en) | Method for calibrating a camera in an orthogonal three-coordinate measuring machine | |
CN102359780A (en) | Ground target positioning method applied into video monitoring system | |
CN103679693A (en) | Multi-camera single-view calibration device and calibration method thereof | |
CN105469389A (en) | Grid ball target for visual sensor calibration and corresponding calibration method | |
CN104697463A (en) | Blanking feature constraining calibrating method and device for binocular vision sensor | |
CN102930551B (en) | Camera intrinsic parameters determined by utilizing projected coordinate and epipolar line of centres of circles | |
CN104655106B (en) | Autonomous positioning based on GPS RTK and full-view image orients plotting method | |
CN102136140A (en) | Rectangular pattern-based video image distance detecting method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant |