CN106503605A - Human body target recognition methods based on stereovision technique - Google Patents

Abstract

The invention discloses a kind of human body target recognition methods based on stereovision technique, including：The picture that same scene is obtained from two different angles simultaneously by two cameras, forms stereo pairs；The inside and outside parameter of video camera is determined by camera calibration, imaging model is established；Using the matching algorithm based on window, window is created centered on the point to be matched of wherein piece image, identical sliding window is created on another piece image, sliding window is moved in units of pixel successively along EP point, calculation window match measure, optimal match point is found, and the three-dimensional geometric information for target being obtained by principle of parallax generates depth image；Using One-Dimensional Maximum-Entropy thresholding method, head and shoulder information is distinguished in conjunction with gray feature, recognize human body target.Amount of calculation of the present invention is little, can fast and accurately identify human body target with simple image.

Description

Human body target recognition methods based on stereovision technique

Technical field

The present invention relates to a kind of human body target recognition methods, particularly a kind of human body target identification based on stereovision technique Method.

Background technology

With the quick raising of the aspect performance such as Computer Storage, computing, computer is progressively applied to and realizes scene by people The sophisticated functions such as reconstruct, target identification, human-computer interaction, this have not only opened up the scale of computer application field and research side To, and promote the fast development of related discipline.Used as the research field that enlivens now, the essence of computer vision is just It is to replace human eye using video camera, replaces brain using computer, target is identified tracking, and makes corresponding figure Analyzing and processing, generates the image for being suitable for instrument detection or eye-observation.Video technique can be continuously transmitted within a period of time Image, comprising more details information, while it have the advantages that directly perceived, concrete, disposable.Identification to video object Become the important topic in the fields such as image procossing, pattern-recognition, human-computer interaction, be widely used in manufacturing industry, doctor Treat in the various intelligence systems in the fields such as diagnosis, military affairs.

Traditional APC system mainly have pressure capsule system and infrared block system, rapid with laser infrared is sent out Exhibition, detects the signal that human body sends using suitable heat release infrared probe, is identified counting.When target is walked about, Change caused by infrared sensor detection human body infrared spectrum obtains the process of human body target motion, by signal transacting Can be with discrimination objective, their low costs are simple to operate, but there is identification statistics inaccurately, and application places are limited Etc. problems.

Image processing method can also be used for solving the problems, such as human bioequivalence simultaneously.But most methods are only using the one of two dimensional image A little recognizers, such as choose some parts of human body as feature, it is intended to mated in the picture, so as to reach knowledge Other purpose.At present, the conventional method of human bioequivalence also has a lot：Based on manikin, the method for structural element, should The method of kind has higher requirement to the image information for extracting whole people, and the object of motion deformation is difficult to process, and to figure Requirement of real-time height as collection；Based on wavelet transformation and the method for SVMs, the method is mainly based upon small echo mould The principle of plate, needs to search for whole image according to different scale sizes, computationally intensive.

Content of the invention

It is an object of the invention to provide a kind of human body target recognition methods based on stereovision technique.

The technical solution for realizing the object of the invention is：A kind of human body target recognition methods based on stereovision technique, Comprise the following steps：

Step 1, the picture for being obtained same scene by two cameras from two different angles simultaneously, form stereo-picture Right；

Step 2, the inside and outside parameter of video camera is determined by camera calibration, establish imaging model；

The matching algorithm of step 3, employing based on window, creates window centered on the point to be matched of wherein piece image, Identical sliding window is created on another piece image, and sliding window is moved in units of pixel successively along EP point, Calculation window match measure, finds optimal match point, the three-dimensional geometric information for obtaining target by principle of parallax, generates deep Degree image；

Step 4, One-Dimensional Maximum-Entropy thresholding method is adopted, head and shoulder information is distinguished in conjunction with gray feature, recognize people Body target.

The present invention compared with prior art, its remarkable advantage：

(1) present invention is little in the human body target recognition methods amount of calculation of stereovision technique, can use simple image Human body target is fast and accurately identified；

(2) present invention can be identified using the depth information of image in the case of crowded, effectively excluded dry Disturb, differentiate moving target.

Description of the drawings

Fig. 1 is human body target recognition methods FB(flow block) of the present invention based on stereovision technique.

Fig. 2 is original depth-map in the embodiment of the present invention.

Fig. 3 is using the human body target identification figure obtained after the inventive method process in the embodiment of the present invention.

Specific embodiment

In conjunction with Fig. 1, the human body target recognition methods based on stereovision technique of the present invention, comprise the following steps：

Step 1, the acquisition of stereo pairs：

Two MTV-1881EX-3 cameras are placed in parallel, from two different angles while obtaining the picture of same scene, Form stereo pairs；

Step 2, the inside and outside parameter of video camera is determined by camera calibration, establish imaging model, specially：

Step 2-1, camera coordinates are demarcated, calibration figure is gridiron pattern, calibration principle is as follows：

Assume z=0 world coordinate system plane be stencil plane, [r₁r₂r₃] sit relative to the world for camera coordinate system Mark system spin matrix, t be camera coordinate system relative to world coordinate system translation vector, [X Y 1]^TFor point in template Homogeneous coordinates, [u υ 1]^TFor the homogeneous coordinates on the spot projection on stencil plane to the plane of delineation, K is represented in video camera Ginseng matrix；

Step 2-2, set camera coordinate system Ox_cy_cz_cFor the rectangular coordinate system being fixed on video camera, its origin O definition For the photocentre of video camera, x_c, y_cAxle is respectively parallel to the x of image physical coordinates system, y-axis, z_cAxle and optical axis coincidence, i.e., z_cImaging plane of the axle perpendicular to video camera, photocentre is to the plane of delineation apart from OO₁Effective focal length f for video camera；

Step 2-3, set (x_w, y_w, z_w) for certain P point in three-dimensional world coordinate system three-dimensional coordinate, (x_c, y_c, z_c) it is three-dimensional coordinate of same point P in camera coordinate system, the point in world coordinate system is to video camera The conversion of coordinate system is expressed as by orthogonal spin matrix R and translation transformation matrix T：

Wherein, R is 3 × 3 spin matrixs, translation matrix

Orthogonal matrix R is that optical axis is combined relative to the direction cosines of world coordinate system reference axis, comprising three independent angles Variable (Eulerian angles)：ψ angles (driftage) are rotated around x-axis；θ angles (pitching) are rotated around y-axis；φ angles (side is rotated around z-axis Incline), add three variables of T totally six parameters, referred to as video camera external parameter；

Step 2-4, the rigid transformation homogeneous coordinates of world coordinate system and camera coordinate system and matrix form are reduced to：

Therefore, can be with a matrix M between world coordinate system and camera coordinate system₂To represent, as long as known M₂Just The conversion of coordinate can be carried out between two coordinate systems；

Camera coordinates are tied to the preferable perspective projection transformation under the conversion i.e. pin-hole model of preferable image physical coordinates system, There is following formula to set up：

X=f x_c/z_cY=f y_c/z_c

X, y are respectively the abscissa and ordinate of preferable image physical coordinates system；

Equally represent that above formula is with homogeneous coordinates and matrix：

Ideal image coordinate is tied to the conversion of image pixel coordinates system, is indicated with homogeneous coordinates：

Wherein, u₀、v₀Represent the coordinate of camera coordinate system；

Its reverse-power is：

Above-mentioned relation formula is substituted into, it is possible to obtain the coordinate (u, υ) of P point coordinates that world coordinate system represents and its projection P ' Between relation：

Wherein, α=f/dx=f s_x/d_y, β=f/dy_；M₁For inner parameter battle array, M₂For external parameter battle array, M is 3 × 4 matrix, referred to as projection matrix, characterize the fundamental relation between two dimensional image coordinate and three-dimensional world coordinate, it is known that thing Point World coordinates using the matrix can to obtain corresponding ideal image coordinate, whereas if be aware of Metzler matrix and The image coordinate of picture point, it is possible to obtain by a space ray corresponding to video camera photocentre；

The fundamental relation between two dimensional image coordinate and three-dimensional world coordinate is obtained, that is, completes the demarcation of video camera；Video camera The image coordinate of acquisition can be converted to the coordinate that three-dimensional world coordinate is fastened by this imaging model unification, that is, determine The imaging model that video camera gained image is fastened in three-dimensional world coordinate.

The matching algorithm of step 3, employing based on window, creates window centered on the point to be matched of wherein piece image, Identical sliding window is created on another piece image, and sliding window is moved in units of pixel successively along EP point, Calculation window match measure, finds optimal match point, the three-dimensional geometric information for obtaining target by principle of parallax, generates deep Degree image；Specifically include following steps：

Step 3-1, hypothesis on the basis of right figure are made the difference with background, are obtained foreground picture；

Step 3-2, the determination of parallax：

The first step, in foreground picture, it is assumed that on the basis of right figure, calculates each pixel corresponding with left figure on given parallax The gray scale difference value of point；

Second step, on each parallax, is changed to using the narrow bar window perpendicular to base direction, using based on window Matching algorithm calculates the gray scale difference value of the window centered on each pixel and expression formula is as follows：

In formula, sizes of the m*n for template window, unit lengths of the γ for template window, unit width of the δ for template window Degree, I_right[x_e+ γ, y_e+ δ] it is right view [x_e+ γ, y_e+ δ] gray value at coordinate, I_left[x_e+ γ+d, y_e+ δ] it is left view [x_e+ γ+d, y_e+ δ] gray value at coordinate, d is parallax；

D, in set disparity range, is got maximum disparity from minimum parallax, successively comparison expression by the 3rd step Value, the minimum corresponding point of value is optimal match point, the parallax value of corresponding parallax value as the pixel；

Step 3-3, the depth information for determining target：

Binocular range finding has mainly used the impact point difference that the lateral coordinates of imaging are directly present on two width views of left and right (i.e. parallax) and impact point have inversely proportional relation to imaging plane apart from Z, when video camera focal length known to In the case of, the depth information of any point, the i.e. coordinate value of the Z axis under camera coordinate system, if b is two cameras Optical center distance；Target Q to camera vertical range be H；Identical focal length is f, Q₁、Q₂It is target Q in video camera Imaging point；D is parallax, it is assumed that the optical axis of two video cameras is parallel to each other, and is derived from similar triangles：

H=(b × f))/d

Target Q for obtaining is the depth information of target to camera vertical range；

So, stereoscopic vision is counted and can pass through triangle meter using two or more than two video cameras for having position skew Calculate, obtain the depth information of place scene, on condition that requiring that the point in scene all has picture point in left images；On a left side In right view, the position of picture point is different, that is, parallax, and the point in scene is different with a distance from video camera, parallax It is also different, parallax diminishes big with the distance from camera；Binocular stereo vision is based on this parallax, Determine object to the distance of video camera using triangulo operation.

Step 4, One-Dimensional Maximum-Entropy thresholding method is adopted, head and shoulder information is distinguished in conjunction with gray feature, recognize people Body target, specially：

Step 4-1, the sub-box that depth image is divided into L*L pixels, L are positive integer, and with nine grids are Unit, with from left to right when mobile, order from top to bottom often compares once, the sub-box of a mobile L*L pixel, If the average gray of middle grid is higher than surrounding eight neighborhood average gray, it is head target area to establish middle grid；

Step 4-2, to head target area given threshold binaryzation, split head target；Specially：

Head and non-head region are split using One-Dimensional Maximum-Entropy thresholding method, p is made_iRepresent picture of the gray value for i in image Ratio shared by element, with gray level t as Threshold segmentation head and shoulder regions, is higher than the pixel structure of t gray levels in region Into head zone, non-head region is constituted less than the pixel of gray level t, then the entropy difference of non-head region and head zone It is defined as:

H_O=-Σ_i[p_i/(1-p_t)]lg[p_i/(1-pt)]

Wherein：Wherein i represents the gray value (0≤i≤255) of pixel, H_t=-Σ_tp_ilgp_i, H_E=-Σ_ip_ilgp_i, when entropy function value andWhen obtaining maximum, gray level t can be used as segmentation figure The threshold value of picture:

T=arg { max { H_B+H_o}}

Step 4-3, the average gray and gray variance that determine the head zone after splitting：

Wherein M, N represent that the ranks number in each region, ε, ∈ represent that unit ranks number, f (ε, ∈) are represented respectively respectively The gray value that (ε, ∈) puts, when gray variance is more than the threshold value for setting, filters the pixel；

Step 4-4, according to human body head, under different field heights, whether the ratio of total pixel width and field height meets The scope of setting filters the long and narrow pseudo- target of profile.

The geometric properties of head mainly have class ellipticalness, head area, long width etc.；By continuous emulation testing In the scope of the total pixel width of certain field height head portion, at the same by emulation can obtain the scope of w/h for [0.65, 1.5], differentiated by such threshold value, the long and narrow pseudo- target of profile can be effectively filtered out.

With reference to specific embodiment, the invention will be further described.

Embodiment

In conjunction with the original depth image shown in Fig. 2, to visible ray in the case of, human hair more black and dark clothes or Human hair color is shallower, and humanbody moving object when easy and background is obscured again is used and carried out based on stereoscopic vision algorithm Process.

Fig. 3 is the segmentation figure picture using human body target head shoulder after the inventive method process, significantly can find out, to can The result for seeing moving target in the case of light is, when human hair is more black and dark clothes or human hair color compared with Shallow, again easily and when background obscures, the depth image accuracy of identification height of stereovision technique output, be difficult by light and The impact of background, it will be apparent that distinguished target and background.

Claims (4)

1. a kind of human body target recognition methods based on stereovision technique, it is characterised in that comprise the following steps：

Step 1, the picture for being obtained same scene by two cameras from two different angles simultaneously, form stereo-picture Right；

Step 2, the inside and outside parameter of video camera is determined by camera calibration, establish imaging model；

The matching algorithm of step 3, employing based on window, creates window centered on the point to be matched of wherein piece image, Identical sliding window is created on another piece image, and sliding window is moved in units of pixel successively along EP point, Calculation window match measure, finds optimal match point, the three-dimensional geometric information for obtaining target by principle of parallax, generates deep Degree image；

Step 4, One-Dimensional Maximum-Entropy thresholding method is adopted, head and shoulder information is distinguished in conjunction with gray feature, recognize people Body target.

2. the human body target recognition methods based on stereovision technique according to claim 1, it is characterised in that Step 2 is specially：

Step 2-1, camera coordinates are demarcated, calibration figure is gridiron pattern, calibration principle is：

$\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=K\left[\begin{array}{cccc}{r}_1& r_2& r_3& t\end{array}\right]\left[\begin{array}{c}X\\ Y\\ 0\\ 1\end{array}\right]=K\left[\begin{array}{ccc}{r}_1& r_2& t\end{array}\right]\left[\begin{array}{c}X\\ Y\\ 1\end{array}\right]$

Assume z=0 world coordinate system plane be stencil plane, [r₁r₂r₃] sit relative to the world for camera coordinate system Mark system spin matrix, t be camera coordinate system relative to world coordinate system translation vector, [X Y 1]^TFor point in template Homogeneous coordinates, [u v 1]^TFor the homogeneous coordinates on the spot projection on stencil plane to the plane of delineation, K is represented in video camera Ginseng matrix；

Step 2-2, set camera coordinate system Ox_cy_cz_cFor the rectangular coordinate system being fixed on video camera, its origin O definition For the photocentre of video camera, x_c, y_cAxle is respectively parallel to the x of image physical coordinates system, y-axis, z_cAxle and optical axis coincidence, i.e., z_cImaging plane of the axle perpendicular to video camera, photocentre is to the plane of delineation apart from OO₁Effective focal length f for video camera；

Step 2-3, set (x_w, y_w, z_w) for certain P point in three-dimensional world coordinate system three-dimensional coordinate, (x_c, y_c, z_c) it is three-dimensional coordinate of same point P in camera coordinate system, the point in world coordinate system is to video camera The conversion of coordinate system is expressed as by orthogonal spin matrix R and translation transformation matrix T：

$\left[\begin{array}{c}{x}_c\\ y_c\\ z_c\end{array}\right]=R\left[\begin{array}{c}{x}_w\\ y_w\\ z_w\end{array}\right]+T$

Wherein, R is 3 × 3 spin matrixs, translation matrix $T=\left[\begin{array}{c}{t}_x\\ t_y\\ t_z\end{array}\right];$

Orthogonal matrix R is that optical axis is combined relative to the direction cosines of world coordinate system reference axis, comprising three independent angles Variable：ψ angles rotated around x-axis, rotate θ angles around y-axis and rotate φ angles around z-axis, be referred to as outside video camera with three variables of T Portion's parameter；

Step 2-4, the rigid transformation homogeneous coordinates of world coordinate system and camera coordinate system and matrix form are reduced to：

$\left[\begin{array}{c}{x}_c\\ y_c\\ z_c\\ 1\end{array}\right]=\left[\begin{array}{cc}R& T\\ 0^T& 1\end{array}\right]\left[\begin{array}{c}{x}_w\\ y_w\\ z_w\\ 1\end{array}\right]=M_2\left[\begin{array}{c}{x}_w\\ y_w\\ z_w\\ 1\end{array}\right]$

$\widetilde{x_c}=\left[\begin{array}{cc}R& T\\ 0^T& 1\end{array}\right]\widetilde{x_w}\⇒\widetilde{x_c}=M_2\widetilde{x_w}\⇒\widetilde{x_w}=M_2^{-1}\widetilde{x_c}$

Camera coordinates are tied to the preferable perspective projection transformation under the conversion i.e. pin-hole model of preferable image physical coordinates system, There is following formula to set up：

X=f x_c/z_cY=f y_c/z_c

X, y are respectively the abscissa and ordinate of preferable image physical coordinates system；

Equally represent that above formula is with homogeneous coordinates and matrix：

$z_c\left[\begin{array}{c}x\\ y\\ 1\end{array}\right]=\left[\begin{array}{cccc}f& 0& 0& 0\\ 0& f& 0& 0\\ 0& 0& 1& 0\end{array}\right]\left[\begin{array}{c}{x}_c\\ y_c\\ z_c\\ 1\end{array}\right]$

Ideal image coordinate is tied to the conversion of image pixel coordinates system, is indicated with homogeneous coordinates：

$\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\left[\begin{array}{ccc}1/dx& 0& u_0\\ 0& 1/dy& v_0\\ 0& 0& 1\end{array}\right]\left[\begin{array}{c}x\\ y\\ 1\end{array}\right]=\left[\begin{array}{ccc}{s}_x/dy& 0& u_0\\ 0& 1/dy& v_0\\ 0& 0& 1\end{array}\right]\left[\begin{array}{c}x\\ y\\ 1\end{array}\right]$

Its reverse-power is：

$\left[\begin{array}{c}x\\ y\\ 1\end{array}\right]=\left[\begin{array}{ccc}dy/s_x& 0& -u_{0}dy/s_x\\ 0& dy& -v_{0}dy\\ 0& 0& 1\end{array}\right]\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]$

Obtain the relation between the coordinate (u, v) of P point coordinates that world coordinate system represents and its projection P '：

$\begin{array}{c}{z}_c\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\left[\begin{array}{ccc}{s}_x/dy& 0& u_0\\ 0& 1/dy& v_0\\ 0& 0& 1\end{array}\right]\left[\begin{array}{cccc}f& 0& 0& 0\\ 0& f& 0& 0\\ 0& 0& 1& 0\end{array}\right]\left[\begin{array}{cc}R& T\\ 0^T& 1\end{array}\right]\left[\begin{array}{c}{x}_w\\ y_w\\ z_w\\ 1\end{array}\right]\\ =\left[\begin{array}{cccc}\mathrm{\α}& 0& u_0& 0\\ 0& \mathrm{\β}& v_0& 0\\ 0& 0& 1& 0\end{array}\right]\left[\begin{array}{cc}R& T\\ 0^T& 1\end{array}\right]\left[\begin{array}{c}{x}_w\\ y_w\\ z_w\\ 1\end{array}\right]=M_1{M}_2\widetilde{x_w}=M\widetilde{x_w}\end{array}$

Wherein, α=f/dx=f s_x/ dy, β=f/dy；M₁For inner parameter battle array, M₂For external parameter battle array, M is 3 × 4 projection matrix, characterizes the fundamental relation between two dimensional image coordinate and three-dimensional world coordinate.

3. the human body target recognition methods based on stereovision technique according to claim 1, it is characterised in that Step 3 is specially：

Step 3-1, hypothesis on the basis of right figure are made the difference with background, are obtained foreground picture；

Step 3-2, determine parallax：

The first step, in foreground picture, it is assumed that on the basis of right figure, calculates each pixel corresponding with left figure on given parallax The gray scale difference value of point；

Second step, on each parallax, is changed to using the narrow bar window perpendicular to base direction, using based on window Matching algorithm calculates the gray scale difference value of the window centered on each pixel and expression formula is as follows：

${\mathrm{\Σ}}_{\mathrm{\γ}=-\frac{m}{2}}^{\frac{m}{2}}{\mathrm{\Σ}}_{\mathrm{\δ}=-\frac{n}{2}}^{\frac{R}{2}}|I_{right}\[x_e+\mathrm{\γ},y_e+\mathrm{\δ}\]-I_{left}\[x_e+\mathrm{\γ}+d,y_e+\mathrm{\δ}\]|$

In formula, sizes of the m*n for template window, unit lengths of the γ for template window, unit width of the δ for template window Degree, I_right[x_e+ γ, y_e+ δ] it is right view [x_e+ γ, y_e+ δ] gray value at coordinate, I_left[x_e+ γ+d, y_e+ δ] it is left view [x_e+ γ+d, y_e+ δ] gray value at coordinate, d is parallax；

D, in set disparity range, is got maximum disparity from minimum parallax, successively comparison expression by the 3rd step Value, the minimum corresponding point of value is optimal match point, the parallax value of corresponding parallax value as the pixel；

Step 3-3, the depth information for determining target：

The focal length of known video camera, the depth information i.e. coordinate value of the Z axis under camera coordinate system of any point, If b is two camera optical center distances；Target Q to camera vertical range be H；Identical focal length is f, Q₁、Q₂Point Not Wei target Q two video cameras imaging point；D is parallax, it is assumed that the optical axis of two video cameras is parallel to each other, by phase Derive like triangle and understand：

H=(b × f)/d

Target Q for obtaining is the depth information of target to camera vertical range.

4. the human body target recognition methods based on stereovision technique according to claim 1, it is characterised in that Step 4 is specially：

Step 4-1, the sub-box that depth image is divided into L*L pixels, L are positive integer, and with nine grids are Unit, with from left to right when mobile, order from top to bottom often compares once, the sub-box of a mobile L*L pixel, If the average gray of middle grid is higher than surrounding eight neighborhood average gray, it is head target area to establish middle grid；

Step 4-2, to head target area given threshold binaryzation, split head target；Specially：

Head and non-head region are split using One-Dimensional Maximum-Entropy thresholding method, p is made_iRepresent picture of the gray value for i in image Ratio shared by element, with gray level t as Threshold segmentation head and shoulder regions, is higher than the pixel structure of t gray levels in region Into head zone, non-head region is constituted less than the pixel of gray level t, then the entropy difference of non-head region and head zone It is defined as:

$H_B=-{\mathrm{\Σ}}_i\left(\frac{p_i}{p_t}\right)\lg \left(\frac{p_i}{p_t}\right)$

H_O=-Σ_i[p_i/(1-p_t)]lg[p_i/(1-p_t)]

Wherein,I represents the gray value (0≤i≤255) of pixel, H_t=-Σ_tp_ilgp_i,H_E=-Σ_ip_ilgp_i, when entropy function value andWhen taking maximum, gray level t as segmentation figure as Threshold value:

T=arg (max { H_B+H_O}}

Step 4-3, the average gray and gray variance that determine the head zone after splitting：

$\stackrel{\‾}{g}=\frac{{\mathrm{\Σ}}_{\mathrm{\ϵ}=0}^{M-1}{\mathrm{\Σ}}_{\mathrm{\ϵ}=0}^{M-1}f(\mathrm{\ϵ},\mathrm{\ϵ})}{M*N}$

$\mathrm{var}=\frac{{\mathrm{\Σ}}_{\mathrm{\ϵ}=0}^{M-1}{\mathrm{\Σ}}_{\mathrm{\ϵ}=0}^{M-1}{\left(f\right(\mathrm{\ϵ},\mathrm{\ϵ})-\stackrel{\‾}{g})}^2}{M*N}$

Wherein M, N represent that the ranks number in each region, ε, ∈ represent that unit ranks number, f (ε, ∈) are represented respectively respectively The gray value that (ε, ∈) puts, when gray variance is more than the threshold value for setting, filters the pixel；

Step 4-4, according to human body head, under different field heights, whether the ratio of total pixel width and field height meets The scope of setting filters the long and narrow pseudo- target of profile, obtains human body target.