CN105173102A - Four-rotor aircraft stability augmentation system and method based on multiple images - Google Patents

Abstract

The invention discloses a four-rotor aircraft stability augmentation system and a four-rotor aircraft stability augmentation method based on multiple images. Three controllable cameras are mounted on the aircraft and are distributed on three coordinate axes of the aircraft, so that multi-direction image acquiring is realized; multiple images which are taken, at certain moment, by all cameras are taken as a reference image, a frame image, which is obtained within each shooting period after three selected areas are selected on fixed coordinate positions in each image by virtue of image processing, is compared with a target specified in the reference image; and coordinate positions of the target in each frame are compared and analyzed to obtain posture changes and position changes of the aircraft, so that stability augmentation of the aircraft is realized by virtue of gesture feedback and position feedback. According to the four-rotor aircraft stability augmentation system and the four-rotor aircraft stability augmentation method based on multiple images, keeping of a permanent position, in farmland regional operation, of the aircraft can be realized, and spraying precision and reliability of pesticides are improved.

Description

A kind of quadrotor stability augmentation system based on many images and method

Technical field

The present invention relates to the technology application of aircraft and unmanned plane position stability when the operation of zonule, farmland, a kind of particularly quadrotor stability augmentation system based on many images and method.

Background technology

Along with the development of modern agriculture, the application in monitoring agricultural land, crop growth situation and irrigation and water conservancy situation of aircraft and unmanned plane is further extensive, on the basis that it gathers agricultural land information, accomplish accurately to apply fertilizer and spray insecticide, reduce costs, increase reliability.And these requirements will be realized, just need aircraft keep in operation process height and attitude stablize.At present, stability augmentation system plays more and more important effect in the control system of small aircraft.The stability augmentation system of small aircraft, except increasing steady algorithm, also requires that the volume of physical system is little, lightweight.Current digital stability augmentation system application is more, formation mainly micro controller system and the peripheral circuit of digital stability augmentation system, peripheral circuit adopts the three-axis gyroscope sensors such as mpu6050, stability augmentation system mainly carrys out by algorithm the feedback signal that processing instruction signal and sensor return, and transmit control signal to the corresponding performance element of aircraft, aircraft can be flown at steady state.But for digital stability augmentation system, its circuit is built sometimes too complicated, and sensor signal is easily by external interference, and algorithm is comparatively complicated, easily causes signal errors larger when process level is too much.And for polyphaser stability augmentation system, image now can be utilized to determine the attitude of aircraft, reduce sampling and departure, to improve control accuracy.

Summary of the invention

In order to overcome the shortcoming of above-mentioned prior art, the object of the present invention is to provide a kind of quadrotor stability augmentation system based on many images and method, adopt polyphaser to realize the sampling of aircraft surrounding environment, after image procossing, comparative analysis draws position of aircraft and attitudes vibration and then sends and adjusts instruction accordingly, and the increasing realizing aircraft with this is steady.

To achieve these goals, the technical solution used in the present invention is:

Based on a quadrotor stability augmentation system for many images, comprising:

Be arranged on front side of aircraft, three controlled cameras with infrared remote receiver on left side and right side, when aircraft arrives demand height, the order that each camera utilizes infrared remote receiver to receive remote controller carries out image acquisition according to setting cycle;

Be arranged on the image processor that carry-on and described three controlled cameras are connected, the image of image processor to each collected by camera extracts, at stationary coordinate position selected target thing in every width image, image processor follows the tracks of the object of defined in each camera respectively based on MeanShift algorithm;

Be arranged on the micro controller system that carry-on and described image processor is connected, micro controller system receives the result of calculation of described image processor, with the image of each camera first time shooting for benchmark, thereafter in each camera each cycle, the object of the image of shooting all compares with the coordinate position of corresponding original object thing, by the image comparison in every side different shooting cycle, carry out attitude and the position judgment of aircraft, and the adjustment of aircraft is controlled according to judged result output signal, reach attitude, position feedback, this process and then to realize the increasing of aircraft steady repeatedly.

Present invention also offers a kind of quadrotor based on many images and increase steady method, comprise the steps:

Step 1: on front side of aircraft, left side and right side arrange a controlled camera with infrared remote receiver respectively, and when aircraft arrives demand height, the order that each camera utilizes infrared remote receiver to receive remote controller carries out image acquisition according to setting cycle;

Step 2: each image transmitting gathered is to being arranged on carry-on image processor, two-dimensional direct angle coordinate system is set up in every width image, and select three objects in stationary coordinate position, based on the object in each constituency of MeanShift algorithm keeps track every width image;

Step 3: with the image of each camera first time shooting for benchmark, thereafter in each camera each cycle, the object of the image of shooting all compares with the coordinate position of corresponding original object thing, if the changes in coordinates of three objects is no more than default error amount in each width image, then think aircraft steadily hovering, namely attitude does not change, if the coordinate of three objects in certain piece image or multiple image changes simultaneously, then think that attitude of flight vehicle changes, by the position difference of the object of front and back picture, thus the controlling quantity calculated needed for attitude of flight vehicle adjustment, to control the attitude of aircraft, guarantee aircraft is stablized,

Step 4: according to the judged result of step 3, carries out gesture stability to aircraft, and polyphaser and image procossing repeatedly work and make aircraft constantly carry out pose adjustment, until meet biased error with each object of benchmark.

In described step 2, apply the target tracking algorism based on MeanShift, two-dimensional direct angle coordinate system is set up in every width image, and select three objects in stationary coordinate position, extract the various features of two field picture, significantly schemed, namely the two dimensional image that a width is identical with original image size, each pixel value wherein represents the significance size of original image corresponding point, sets up the histogram of target model on this basis, then uses MeanShift algorithm to follow the tracks of.

In described step 2, the object that three coordinates are fixing is chosen in the initial frame image of catching at each camera respectively, coordinate is respectively (x1, y1), (x2, y2) and (x3, y3), hypothetical target thing initial position is at coordinate center, by comparing the side-play amount of object coordinate position in three groups of video sequences respectively, thus differentiate the state of aircraft.

Compared with prior art, the invention has the beneficial effects as follows:

(1) lasting accuracy is improved, the steady precision of increasing of image-pickup method is Pixel-level, and existing electronic type stability augmentation system is by electromagnetic interference, sensor accuracy etc., and to affect precision be decimeter grade, and limit by sensor, slow self drift cannot be monitored and correct.

(2) carry out image acquisition by installing polyphaser, treater carries out image procossing and flying vehicles control, avoids the designing and employing to Various Complex circuit.

(3) adopt algorithm to carry out flying vehicles control, increase useful life, reduce the possibility because hardware damage causes control action to run.

(4) use polyphaser can not affect to some extent the collection of original surrounding environment, reduce the aircraft maloperation because the extraneous errors such as image cause.

(5) part category is few, and data transmission is reliable, and precision is higher, is applicable to comparatively complex environment.

Accompanying drawing explanation

Fig. 1 is aircraft of the present invention and camera fixed position schematic diagram.

Fig. 2 be the present invention when choosing picture coordinate axle set up schematic diagram.

Fig. 3 is the reference base picture that the present invention chooses, fixing constituency, the image of three corresponding left, front, right three cameras of frame difference.

Fig. 4 is the attitude picture 1 that the present invention chooses, and follows the trail of constituency, the image of three corresponding left, front, right three cameras of frame difference.

Fig. 5 is the attitude picture 2 that the present invention chooses, and follows the trail of constituency, the image of three corresponding left, front, right three cameras of frame difference.

Fig. 6 is Meanshift algorithm flow schematic diagram of the present invention.

Detailed description of the invention

Embodiments of the present invention are described in detail below in conjunction with drawings and Examples.

As shown in Figure 1, quadrotor of the present invention comprises four-axle aircraft, treater, three cameras and other controllers.Device concentrates on aircraft center 4 except camera, and camera A, 5 is contained in the front end of aircraft, and camera B, 6 is contained in the left side of aircraft, and camera C, 7 is contained in the right side of aircraft.Four-axle aircraft four axle structurally have employed the basic motive source that four rotors 1 are flight, and four rotors 1 are connected to aircraft center 4 by support 3.Simultaneously four rotors 1 are symmetrically distributed in front and back and the left and right four direction of fuselage between two, four rotors 1 are positioned at same level height, and the radius of each rotor 1 and structure are all the same, one group of relative rotor 1 anticlockwise direction rotates, another is organized relative rotor 1 clockwise direction and rotates, four brushless motors 2 are symmetrically distributed in the end of aircraft parking stand, and the central crossing spatial of support can lay the outside equipment expanded of flight attitude control processor and sensor and other.Because two groups of rotors 1 rotate in the opposite direction, therefore, when aircraft trimmed flight, the aerodynamic force moment of torsion effect of generation and gyro effect are all cancelled out each other, therefore, four-axle aircraft can resist certain external disturbance, ensures the stable of self and the control by remote controller.As can be seen from said structure and principle, the physical construction simple, intuitive of four-axle aircraft and polyphaser thereof, can control flexibly, is for increasing steady best equipment.

Polyphaser stability augmentation system based on four-axle aircraft of the present invention is made up of three small-sized controlled cameras.The image acquisition ability of polyphaser is the necessary condition of native system, is also the notable feature of native system simultaneously.Native system utilizes existing diaxon steering wheel The Cloud Terrace support and regulate system of the present invention.

When aircraft needs position to keep, remote controller sends signal, and each camera accepts instruction to start to take.And camera settings has the shooting cycle, the image transmitting collected, in treater, is often opened the process of image and utilizes MeanShift algorithm keeps track to go out the object often opening defined in image.After first time shooting, left and right, front side image is transferred in image processor carries out process and is significantly schemed and regulation system of axes, and the setting coordinate of the remarkable figure of each camera as shown in Figure 2.Three objects in reality are from left to right chosen in certain two field picture of catching at each camera respectively, the coordinate of the image of each camera is respectively (x1, y1), (x2, y2), (x3, y3), follows the tracks of initial target thing and obtains three new object coordinates after after this each shooting.Before and after the object of catch same camera, coordinate information compares, thus judges the change in location of aircraft.

As shown in Figure 3, this figure is initial pictures.

Contrast with Fig. 4, the coordinate y value of three objects in the image of three cameras all increases, and result illustrates that whole aircraft level height adds.

Contrast with Fig. 5, the coordinate y value of three objects in left camera all increases, three objects of front camera from left to right coordinate y value increase successively, constant, reduce, three object coordinate y values of right camera all reduce, and result illustrates and raises on the left of aircraft, right side reduces, and namely inclination to the right occurs aircraft.

Give an order according to comparative result controller and to adjust aircraft, during as Fig. 4 result occurs, four gyroplane rotate speeds controlling aircraft reduce simultaneously, ensure not deflect while height reduction.During as Fig. 5 result occurs, the gyroplane rotate speed controlling aircraft left camera place reduces, and the gyroplane rotate speed at right camera place raises, and front and back gyroplane rotate speed keeps, thus regulates the inclination of aircraft.Adjustment process simultaneously polyphaser real-time image acquisition information to go forward side by side row relax, repeatedly regulate, until attitude of flight vehicle is stablized.

The present invention increases steady method, comprises the steps:

Step 1: on front side of aircraft, left side and right side arrange a controlled camera with infrared remote receiver respectively, and when aircraft arrives demand height, the order that each camera utilizes infrared remote receiver to receive remote controller carries out external image collection according to setting cycle;

Step 2: each image transmitting gathered is to being arranged on carry-on image processor, two-dimensional direct angle coordinate system is set up in every width image, and select three objects in stationary coordinate position, based on the object in each constituency of MeanShift algorithm keeps track every width image;

How to realize, to the tracking of object, being implemented as follows:

(1) vision mode builds:

The present invention chooses Itti vision mode, it is in feature extraction phases, adopt multiple Low Level Vision feature, as color, intensity, edge etc., these features form the concern figure of each feature by gaussian filtering and Center-Surround operator (center---difference around); Then these features are synthesized a width significantly to scheme.

(2) acquisition of remarkable figure:

The step obtaining the remarkable figure of Itti is:

A, extracts color, intensity and edge feature;

B, carries out filtering with Gaussian filter to color, brightness and edge feature image;

C, " center---difference (Centersurrounddifference) and normalization method around ", obtains color, intensity and edge feature figure;

D, merges characteristic pattern and after normalization method, synthesizes visual saliency map.In order to meet the requirement of real-time, mainly adopt color, intensity and edge feature herein.

(3) Visual Feature Retrieval Process:

A, obtains color characteristic figure

HSV model can be expressed as three attribute chromatic information: tone (H), degree of saturation specific humidity (S), brightness (V), and wherein H represents colouring information, the position of namely residing spectral color.Can extract color characteristic by color component (H), formula is as follows:

H(c,s)＝|H(c)ΘH(s)|(1)

Wherein: c ∈ [2,3], s=c+ δ, δ ∈ [3,4]

C is centre scale

δ is the poor scale of center-surrounding

S is around scale

Θ is operation operator

H (c) c level Gauss color characteristic figure

H (s) s level Gauss color characteristic figure

Tone in H representative image, i.e. color information

H (c, s) represents color characteristic figure

B, obtains strength characteristic figure

Intensity can distinguish the edge of white and black, and can strengthen significance between the two, formula is as follows:

I(c,s)＝|I(c)ΘI(s)|(2)

Wherein: c ∈ [2,3], s=c+ δ, δ ∈ [3,4]

C is centre scale

δ is the poor scale of center-surrounding

S is around scale

Θ is operation operator

I (c) c level gaussian intensity characteristic pattern

I (s) s level gaussian intensity characteristic pattern

Brightness in I representative image, i.e. strength information

I (c, s) represents strength characteristic figure

C, obtains edge feature figure

Edge feature is one of important attribute of image, can touch off the profile of target.Structure edge feature figure is one of important step of structure target model, is obtained by following formula:

E(c,s)＝|E(c)ΘE(s)|(3)

Wherein: c ∈ [2,3], s=c+ δ, δ ∈ [3,4]

C is centre scale

δ is the poor scale of center-surrounding

S is around scale

Θ is operation operator

E (c) c level Gauss edge feature figure

E (s) s level Gauss edge feature figure

Marginal information in E representative image

E (c, s) represents edge feature figure

(4) visual saliency map is generated:

Make scale δ=4 of remarkable figure, 12, three aspects in color, intensity and the edge characteristic pattern obtained respectively by formula (1), formula (2) and formula (3), 4 of each aspect characteristic patterns are combined into characteristic remarkable picture, are respectively color characteristic and significantly scheme strength characteristic is significantly schemed edge feature is significantly schemed itti defines normalization method operator N () in a model, is normalized every stack features figure, tried to achieve by following formula respectively:

$\stackrel{\‾}{H}=\underset{c=2}{\overset{3}{\⊕}}\underset{s=c+3}{\overset{c+4}{\⊕}}N\left(H\right(c,s\left)\right)$

$\stackrel{\‾}{I}=\underset{c=2}{\overset{3}{\⊕}}\underset{s=c+3}{\overset{c+4}{\⊕}}N\left(I\right(c,s\left)\right)$

$\stackrel{\‾}{E}=\underset{c=2}{\overset{3}{\⊕}}\underset{s=c+3}{\overset{c+4}{\⊕}}N\left(E\right(c,s\left)\right)$

In formula: ⊕ represents and is added after the corresponding linear interpolation of characteristic remarkable picture adjusts to same size under multiple yardstick, the normalization method operator of N () for defining in Itti model, respectively the saliency value standard of each remarkable figure is normalized to (0,1) interval;

Then will linear combination becomes visual saliency map.

(5) foundation of goal histogram and following principle:

Set up the image that histogram is convenient to capturing to analyze further, after camera obtains video, the tracking target of video lead frame is chosen after being remarkable figure by initial frame image procossing, set up probability model, in significantly figure calculates, decrease the interference of background, reflect the property of the histogram of tracking target more really.Formula is as follows:

${q^{\mathrm{\Λ}}}_u=C\stackrel{n}{\mathrm{\Σ}}k\left(\right|\left|\frac{x_1-x_0}{h}\right|\left|\right)\mathrm{\δ}\[b\left(x_1\right)-u\],u=1,......,m$

Wherein: k is kernel function, m is the number of eigenwert in feature space, and δ is Kronecker function,

B (x ₁) be pixel x ₁characteristic of correspondence value, n is the number of sampling point, and C is normalization coefficient, and h is the bandwidth of kernel function, x ₀for target's center;

Offset target y is described as:

$\widehat{p_u}=C\underset{i=1}{\overset{n}{\Σ}}k\left(\right|\left|\frac{x_1-y}{h}\right|{|}^2)\mathrm{\δ}\[b\left(x_1\right)-u\]$

Therefore the process of tracking target thing can be equivalent to and find optimum y, makes with the most similar, the similarity Bhattacharyya coefficient between them is measured, namely

$\mathrm{\ρ}\left(y\right)=\underset{u=1}{\overset{m}{\Σ}}\sqrt{\widehat{p_u}\left(y\right)\widehat{q_u}}$

In order to more approach the object of motion, adopt iterative algorithm to calculate, formula is as follows:

$\widehat{y_1}=\frac{\underset{i=1}{\overset{n}{\Σ}}{x}_i{w}_{i}g\left(\right|\left|\frac{\widehat{y_0}-x_i}{h}\right|{|}^2)}{\underset{i=1}{\overset{n}{\Σ}}{w}_{i}g\left(\right|\left|\frac{\widehat{y_0}-x_i}{h}\right|{|}^2)}$

In formula for new target's center position, wherein w _ifor feature weight

$w_i=\underset{u=1}{\overset{m}{\Σ}}\sqrt{\frac{\widehat{q_u}}{\widehat{p_u}\left(\widehat{y_0}\right)}\mathrm{\δ}\[b\left(x_i\right)-u\]}$

Iterative process is exactly constantly calculate until the maximum final center namely orientating target as of Bhattacharyya coefficient stops iteration;

Iteration is carried out in each control cycle, when stopping iteration, namely every width image of three cameras has all tracked the new coordinate of object, by calculate the coordinate in each selected district of every width image side-play amount, judge the attitude state of aircraft, thus calculate the adjustment amount of the gesture stability provided aircraft, steady to ensure the increasing of aircraft.

Step 3: with the image of each camera first time shooting for benchmark, thereafter in each camera each cycle, the object of the image of shooting all compares with the coordinate position of corresponding original object thing, if the changes in coordinates of three objects is no more than default error amount in each width image, then think aircraft steadily hovering, namely attitude does not change, if the coordinate of three objects in certain piece image or multiple image changes simultaneously, then think that attitude of flight vehicle changes, by the position difference of the object of front and back picture, thus the controlling quantity calculated needed for attitude of flight vehicle adjustment, to control the attitude of aircraft, guarantee aircraft is stablized,

Step 4: according to the judged result of step 3, to utilizing respective algorithms to carry out gesture stability to aircraft after the judged result of micro controller system receiving processor, polyphaser and image procossing repeatedly work and make aircraft constantly carry out pose adjustment, until meet biased error with each object of benchmark.

The present invention needs using gained period 1 image as benchmark, other analyses of all making comparisons with it, and then judges that position of aircraft changes.

The present invention carries out the continuous adjustment of position of aircraft by the repeatedly operation work of each control treatment equipment.Namely the increasing passing through to realize after repeatedly adjusting aircraft is steady.

It should be noted that, this method is based on polyphaser, to the basic demand of camera is: can two shaft platforms auxiliary under complete accurate acquisition to image, its each index can be different according to different purposes.

The foregoing describe groundwork of the present invention, essential characteristic and basic embodiment, and the mode that have passed explanation instead of restriction is here set forth.It will be apparent to one skilled in the art that, it is apparent that under not departing from the prerequisite of the invention spirit and scope that appended claims limits in itself, other case study on implementation many can be made.

Claims (5)

1., based on a quadrotor stability augmentation system for many images, it is characterized in that, comprising:

Be arranged on front side of aircraft, three controlled cameras with infrared remote receiver on left side and right side, when aircraft arrives demand height, the order that each camera utilizes infrared remote receiver to receive remote controller carries out image acquisition according to setting cycle;

Be arranged on the image processor that carry-on and described three controlled cameras are connected, the image of image processor to each collected by camera extracts, at stationary coordinate position selected target thing in every width image, image processor follows the tracks of the object of defined in each camera respectively based on MeanShift algorithm;

Be arranged on the micro controller system that carry-on and described image processor is connected, micro controller system receives the result of calculation of described image processor, with the image of each camera first time shooting for benchmark, thereafter in each camera each cycle, the object of the image of shooting all compares with the coordinate position of corresponding original object thing, by the image comparison in every side different shooting cycle, carry out attitude and the position judgment of aircraft, and the adjustment of aircraft is controlled according to judged result output signal, reach attitude, position feedback, this process and then to realize the increasing of aircraft steady repeatedly.

2. the quadrotor based on many images increases a steady method, it is characterized in that, comprises the steps:

Step 1: on front side of aircraft, left side and right side arrange a controlled camera with infrared remote receiver respectively, and when aircraft arrives demand height, the order that each camera utilizes infrared remote receiver to receive remote controller carries out image acquisition according to setting cycle;

Step 2: each image transmitting gathered is to being arranged on carry-on image processor, two-dimensional direct angle coordinate system is set up in every width image, and select three objects in stationary coordinate position, based on the object in each constituency of MeanShift algorithm keeps track every width image;

Step 3: with the image of each camera first time shooting for benchmark, thereafter in each camera each cycle, the object of the image of shooting all compares with the coordinate position of corresponding original object thing, if the changes in coordinates of three objects is no more than default error amount in each width image, then think aircraft steadily hovering, namely attitude does not change, if the coordinate of three objects in certain piece image or multiple image changes simultaneously, then think that attitude of flight vehicle changes, by the position difference of the object of front and back picture, thus the controlling quantity calculated needed for attitude of flight vehicle adjustment, to control the attitude of aircraft, guarantee aircraft is stablized,

Step 4: according to the judged result of step 3, carries out gesture stability to aircraft, and polyphaser and image procossing repeatedly work and make aircraft constantly carry out pose adjustment, until meet biased error with each object of benchmark.

3. increase steady method based on the quadrotor of many images according to claim 2, it is characterized in that, in described step 2, apply the target tracking algorism based on MeanShift, two-dimensional direct angle coordinate system is set up in every width image, and select three objects in stationary coordinate position, extract the various features of two field picture, significantly schemed, namely the two dimensional image that a width is identical with original image size, each pixel value wherein represents the significance size of original image corresponding point, set up the histogram of target model on this basis, then MeanShift algorithm is used to follow the tracks of.

4. increase steady method based on the quadrotor of many images according to claim 2, it is characterized in that, in described step 2, in the initial frame image of catching at each camera respectively, choose the object that three coordinates are fixing, coordinate is respectively (x1, y1), (x2, y2) and (x3, y3), hypothetical target thing initial position is at coordinate center, by comparing the side-play amount of object coordinate position in three groups of video sequences respectively, thus differentiate the state of aircraft.

5. increase steady method based on the quadrotor of many images according to claim 2, it is characterized in that, to some cameras, define objective thing and by MeanShift algorithm realization being implemented as follows the tracking of object in two field picture:

(1) vision mode builds:

Choose Itti vision mode;

(2) acquisition of remarkable figure:

A) local spatial information of the forms such as color, intensity and edge is extracted;

B) with Gaussian filter, filtering is carried out to color, intensity and edge feature image;

C) center---difference and normalization method around, obtains color, intensity and edge feature figure;

D) characteristic pattern merged and after normalization method, synthesize visual saliency map;

(3) Visual Feature Retrieval Process:

A. color characteristic figure is obtained

Color characteristic by extract in tone H by, formula is as follows:

H(c,s)＝|H(c)ΘH(s)|(1)

Wherein: c ∈ [2,3], s=c+ δ, δ ∈ [3,4]

C is centre scale

δ is the poor scale of center-surrounding

S is around scale

Θ is operation operator

H (c) represents c level Gauss color characteristic figure

H (s) represents s level Gauss color characteristic figure

Tone in H representative image, i.e. color information

H (c, s) represents color characteristic figure

B. strength characteristic figure is obtained

Strength characteristic is obtained by following formula:

I(c,s)＝|I(c)ΘI(s)|(2)

Wherein: c ∈ [2,3], s=c+ δ, δ ∈ [3,4]

C is centre scale

δ is the poor scale of center-surrounding

S is around scale

Θ is operation operator

I (c) represents c level gaussian intensity characteristic pattern

I (s) represents s level gaussian intensity characteristic pattern

Brightness in I representative image, i.e. strength information

I (c, s) represents strength characteristic figure

C. edge feature figure is obtained

Edge feature is obtained by following formula:

E(c,s)＝|E(c)ΘE(s)|(3)

Wherein: c ∈ [2,3], s=c+ δ, δ ∈ [3,4]

C is centre scale

δ is the poor scale of center-surrounding

S is around scale

Θ is operation operator

E (c) represents c level Gauss edge feature figure

E (s) represents s level Gauss edge feature figure

Marginal information in E representative image

E (c, s) represents edge feature figure

(4) visual saliency map is generated:

The scale of remarkable figure is made to be 4,12, three aspects in color, intensity and the edge characteristic pattern obtained respectively by formula (1), formula (2) and formula (3), 4 of each aspect characteristic patterns are combined into characteristic remarkable picture, are respectively color characteristic and significantly scheme strength characteristic is significantly schemed edge feature is significantly schemed tried to achieve by following formula respectively:

$\stackrel{\‾}{H}=\underset{c=2}{\overset{3}{\⊕}}\underset{s=c+3}{\overset{c+4}{\⊕}}N\left(H\right(c,s\left)\right)$

$\stackrel{\‾}{I}=\underset{c=2}{\overset{3}{\⊕}}\underset{s=c+3}{\overset{c+4}{\⊕}}N\left(I\right(c,s\left)\right)$

$\stackrel{\‾}{E}=\underset{c=2}{\overset{3}{\⊕}}\underset{s=c+3}{\overset{c+4}{\⊕}}N\left(E\right(c,s\left)\right)$

In formula: being added after the corresponding linear interpolation of characteristic remarkable picture adjusts to same size under representing multiple yardstick, the normalization method operator of N () for defining in Itti model, respectively the saliency value standard of each remarkable figure being normalized to (0,1) interval;

Then will linear combination becomes visual saliency map;

(5) foundation of goal histogram and tracking:

It is histogrammic that to set up formula as follows:

$\stackrel{\mathrm{\Λ}}{q_u}=C\stackrel{n}{\mathrm{\Σ}}k\left(\right|\left|\frac{x_1-x_0}{h}\right|\left|\right)\mathrm{\δ}\[b\left(x_1\right)-u\],u=1,......,m$

Wherein: k is kernel function, m is the number of eigenwert in feature space, and δ is Kronecker function,

B (x ₁) be pixel x ₁characteristic of correspondence value, n is the number of sampling point, and C is normalization coefficient, and h is the bandwidth of kernel function, x ₀for target's center;

Offset target y is described as:

$\widehat{p_u}=C\underset{i=1}{\overset{n}{\Σ}}k\left(\right|\left|\frac{x_1-y}{h}\right|{|}^2)\mathrm{\δ}\[b\left(x_1\right)-u\]$

Therefore the process of tracking target thing can be equivalent to and find optimum y, makes with the most similar, the similarity Bhattacharyya coefficient between them is measured, namely

$\mathrm{\ρ}\left(y\right)=\underset{u=1}{\overset{m}{\Σ}}\sqrt{\widehat{p_u}\left(y\right)\widehat{q_u}}$

In order to more approach the object of motion, adopt iterative algorithm to calculate, formula is as follows:

$\widehat{y_1}=\frac{\underset{i=1}{\overset{n}{\Σ}}{x}_i{w}_{i}g\left(\right|\left|\frac{\widehat{y_0}-x_i}{h}\right|{|}^2)}{\underset{i=1}{\overset{n}{\Σ}}{w}_{i}g\left(\right|\left|\frac{\widehat{y_0}-x_i}{h}\right|{|}^2)}$

In formula for new target's center position, wherein w _ifor feature weight

$w_i=\underset{u=1}{\overset{m}{\Σ}}\sqrt{\frac{\widehat{q_u}}{\widehat{p_u}\left(\widehat{y_0}\right)}\mathrm{\δ}\[b\left(x_i\right)-u\]}$

Iterative process is exactly constantly calculate until the maximum final center namely orientating target as of Bhattacharyya coefficient stops iteration;

Iteration is carried out in each control cycle, when stopping iteration, namely every width image of three cameras has all tracked the new coordinate of object, by calculate the coordinate in each selected district of every width image side-play amount, judge the attitude state of aircraft, thus calculate the adjustment amount of the gesture stability provided aircraft, steady to ensure the increasing of aircraft.