CN106780542A - A kind of machine fish tracking of the Camshift based on embedded Kalman filter - Google Patents
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Abstract
The invention discloses a kind of machine fish tracking of the Camshift based on embedded Kalman filter.The complicated underwater environment of machine fish is can adapt to using the present invention, the accuracy of the quick motion tracking of machine fish is improved, real-time is good.The present invention is predicted first with Kalman filter to the position that moving target is likely to occur in next two field picture, is then scanned in the range of relative decrease using Camshift, can effectively strengthen the accuracy to fast-moving target tracking；Then, the observation for being obtained using Camshift is modified to the observation that Kalman filter is obtained, and further improves the accuracy of target following.Contrast prior art, is capable of the real-time and accuracy of Comprehensive consideration target tracking algorism, to reach the purpose that machine fish is accurately tracked in real time to target.
Description
Technical field
The present invention relates to field of intelligent control technology, and in particular to a kind of Camshift based on embedded Kalman filter
Machine fish tracking.
Background technology
Deepened continuously with to marine resources development, bionic machine fish cooperation control system causes concern, and as imitative
The important component vision subsystem of raw machine fish cooperation control system, is the unique information source of decision-making subsystem, vision
Track algorithm determines quick and precisely property and the real-time of target following.
Machine vision comes across in the collection of thesis that 1975 are edited by Winston earliest.The Marr of Britain is taught in 1973
Year at the Massachusetts Institute of Technology (MIT), a new theories of vision research group is created, in 1977, it is proposed that a kind of new
Theory on computer vision-Marr theories of vision, the theory turns into of computer vision research field in the eighties in 20th century
Highly important theoretical frame.It is used for advanced video safety monitoring technology, such as campus monitoring, traffic monitoring, road pedestrian successively
Statistical system, Visual Tracking is in missile imaging guidance, the automatically analyzing of ultrasonic wave and nuclear-magnetism sequence image, man-machine friendship in addition
Mutually, the aspect such as virtual reality, robot visual guidance has extensive important application.Every kind of NI Vision Builder for Automated Inspection be integrated with a lot, than
Such as the real-time target Detection And Tracking under static background and movement background, the Classification and Identification of target, target pose estimation, video camera
Autonomous control, video image processing system, human body gait analysis etc..It is domestic national with Institute of Automation, CAS pattern-recognition
Key lab is that many colleges and universities and research institution of representative also achieve good scientific achievement in field of machine vision.But nothing
By being domestic or external, the early stage treatment to obtained image information is all essentially consisted of in the research emphasis of field of machine vision
(denoising, enhancing, target detection etc.) and multiple video frequency motion targets are carried out in complex environment stabilization, quickly and correctly with
Track.Its difficult point is that present visual task is to recover 3D scenes by 2D images mostly, and due to there is projection in imaging process, hiding
Gear, mixing, the distortion of various scene factors etc., the objective description for wanting to obtain 3D models by the expression of highly structural is very
Difficult, these are to need asking for continuous hoisting machine vision algorithm improvement effect in current machine vision research field all the time
Topic.
In field of machine vision, the groundwork of track algorithm is that expression target area is found in continuous video sequence
Or the continuous correspondence of the picture structure of target signature.Camshift algorithms (Continuously Apative Mean-Shift)
It is a kind of motion tracking algorithms, the purpose of tracking is mainly reached by the colouring information of moving object in video image, can
According to the size for tracking change in shape self-adaptative adjustment search window of the target in motion process, the color using target is made
Be tracking characteristics, rotated in target that also there is certain robustness when being at least partially obscured, it is simple easily realize, amount of calculation is small,
Real-time is good, is an algorithm for practicality for calculating locally optimal solution.But in the unexpected quick motion conditions of complex background and target
Under be likely to occur tracking target occur error it is larger in addition tracking failure problem.
The content of the invention
In view of this, the invention provides a kind of machine fish track side of the Camshift based on embedded Kalman filter
Method, can adapt to the complicated underwater environment of machine fish, improve the accuracy of the quick motion tracking of machine fish, and real-time is good.
Camshift machine fish trackings based on embedded Kalman filter of the invention, comprise the following steps：
Step 1, before target following is carried out, by man-machine interaction mode, with mouse chosen on tracking picture manually with
Track region；
Step 2, builds Kalman filter and initializes；
Wherein, dbjective state vector x=[μ x, μ y, vx, vy]T, wherein, μ x, μ y represent the barycenter of tracked target respectively
X in the rectangular coordinate system Oxy of video image, the coordinate components in y-axis, vx, vy represent speed of the target in x, y-axis respectively
Degree component, i.e., every two frames target movement position represents transposition in x directions and the difference in y directions, subscript T；
Target observation vector y=[μ x, μ y]T；
Systematic state transfer matrixΔ t is the time difference of adjacent two frame；Systematic observation matrix
The process noise covariance matrix Q of system and the covariance matrix R of observation noise are respectively：
Wherein, e is natural logrithm；
Initial time Kalman filter Square Error matrix P0For：
Step 3, the dbjective state vector x at the Kalman filter prediction current k moment built using step 2kAnd target
Observation vector yk；
Step 4, with the dbjective state vector x of step 3 Kalman filter predictionkIn position in region of search
The heart, the target position information at current k moment is obtained using Camshift algorithms, as Camshift observation vectors yk_c；
Step 5, according to formula Kk=P'kCT·(CP'kCT+R)-1Calculate the Kalman filter gain at current k moment
COEFFICIENT Kk, wherein, P'k=APk-1AT+ Q, Pk-1It is k-1 moment Kalman filter Square Error matrix；By Kk, step 3 Kalman
The target observation vector y of filter predictionkAnd the Camshift observation vectors y that step 4 is obtainedk_c, substitute into state revision equation
x′k=xk+Kk(yk_c-yk), obtain revised state vector x 'k；And according to Pk=(1-KkC)P'kUpdate Kalman filtering equal
Square error matrix Pk；
Step 6, the revised state vector x ' that step 5 is obtainedkAs the target-like state value of present frame, read next
Two field picture, performs step 3~step 6.
Further, in the step 3, by state-transition matrix A and last moment dbjective state vector xk-1Substitution system
State equation xk=Axk-1+vk-1In, predict current k moment dbjective state vector xk, wherein, vk-1It is systematic procedure noise vector；
By predicted state vector xkSubstitute into systematic observation equation yk=Cxk+wkIn, predict current k moment target observation vector yk, wherein,
wkIt is observation noise vector.
Further, in the step 4, with the dbjective state vector x of step 3 Kalman filter predictionkIn position
As the center of the region of search at current k moment, according to the H histogram of component of previous frame image target area, in current candidate
In target area, according to Meanshift criterions, best match position is found, target centroid point is obtained, as Camshift
Observation vector yk_c。
Beneficial effect：
The present invention is carried out first with Kalman filter to the position that moving target is likely to occur in next two field picture
Prediction, is then scanned for using Camshift in the range of relative decrease, can effectively be strengthened to fast-moving target
The accuracy of tracking；Then, the observation for being obtained using Camshift is modified to the observation that Kalman filter is obtained,
Further improve the accuracy of target following.
Contrast prior art, is capable of the real-time and accuracy of Comprehensive consideration target tracking algorism, to reach machine
The purpose that fish accurately tracks in real time to target.
Brief description of the drawings
Fig. 1 is flow chart of the present invention.
Specific embodiment
Develop simultaneously embodiment below in conjunction with the accompanying drawings, and the present invention will be described in detail.
The invention provides a kind of Camshift machine fish trackings based on embedded Kalman filter, using being based on
The H histogram of component in hsv color space carries out the lookup of Meanshift algorithms to two field picture currently as matching standard performing
Before the step of target location, Kalman filter is added, completed to this frame using the target optimal location of former frame
The substantially prediction of target location in image, then makes MeanShift algorithms that searching mesh is matched in the neighborhood of this predicted position
Punctuate, the prediction using this impact point as observation to Kalman filter is modified and obtains target location optimal estimation,
The target location optimal estimation that will be obtained afterwards continues iteration carries out position prediction next time.Kalman filter only needs to know
The state vector of previous moment and the observation at current time, it is possible to deduce the state estimation of subsequent time, it is used
Recursive filtering computational methods, it is simple to initialization requirements and amount of calculation is small, so that with Camshift algorithm keeps track machines
It is more directional during device fish, so as to effectively improve the tracking accuracy to motor-driven machine fish while real-time feature is ensured.
Flow of the present invention is as shown in figure 1, mainly include 2 parts：Part i：Kalman filter is built, using former frame
Target position information and current observation in image, carry out target prodiction, and obtain the observation of Kalman target locations
Value；Part ii：Target following positioning is carried out in the target area of Kalman prediction using Camshift algorithms, is obtained
Camshift target locations observation；Ii I parts, see according to Kalman target locations observation and Camshift target locations
The difference of measured value, is modified to Kalman target prodiction values, obtains final target location.
Specifically include following steps：
Step 1, before target following is carried out, by man-machine interaction mode, with mouse chosen on tracking picture manually with
Track region；
Step 2, builds Kalman filter and initializes；
The present invention proposes the Kalman filter model of four dimensional vectors, in the rectangular coordinate system Oxy of gathered video image,
Moving situation to tracked target is predicted.
Wherein, dbjective state vector x=[μ x, μ y, vx, vy]T, wherein, μ x, μ y represent the barycenter of tracked target respectively
Coordinate components in x, y-axis, vx, vy represent velocity component of the target in x, y-axis respectively, i.e., every two frames target movement position
In x directions and the difference in y directions, subscript T represents transposition.
Target observation vector y=[μ x, μ y]T, in machine fish target observation vector, μ x, μ y are represented in present frame respectively
Coordinate components of the target centroid for observing on x, y direction.
In object tracking process, because the time interval of two continuous frames image is shorter, generally tracked target
Motion state change it is relatively small, it is possible to think tracked target adjacent two frame time interval in make uniform motion.
Correlation computations amount formula in the mathematical notation and Kalman filtering in dynamic system states space is as follows：
System state equation：xk=Axk-1+vk-1
Systematic observation equation：yk=Cxk+wk
Predicting covariance matrix：P'k=APk-1AT+Qk-1
Kalman filter gain matrix：Kk=P'kCT·(CP'kCT+Rk)-1
Kalman filter estimate：xk=Axk-1+Kk(yk-CAxk-1)
Kalman filter Square Error matrix：Pk=(1-KkC)P'k
Wherein, xk-1、xkThe dbjective state at respectively k-1, k moment is vectorial, ykIt is the target observation vector at k moment.A is to be
System state-transition matrix；vk-1It is k-1 etching process noise vectors；C is systematic observation matrix；wkIt is observation noise vector；P′kFor
K moment predicting covariance matrixes；Qk-1It is k-1 moment systematic procedure noise covariance matrixs；KkIt is Kalman filter gain
Matrix；RkIt is the covariance matrix of k moment systematic observation noises；xk-1、xkRespectively k-1, k moment Kalman filter estimate；
PkIt is k moment Kalman filter Square Error matrix.
Here the process noise vector v of systemk-1With observation noise vector wkIt is mutually independent, is all that average is zero
Gaussian sequence.It is assumed that process noise vector vk-1Obey distribution vk-1~N (0, Qk-1), wherein Qk-1It is process noise association side
Difference matrix, Qk-1=Qk=Q；Observation noise vector wkObey distribution wk~N (0, Rk), wherein RkIt is the covariance square of observation noise
Battle array, Rk-1=Rk=R.
In machine fish dbjective state vector, according to the characteristics of multiple robot fish system experimental system, corresponding ginseng is extrapolated
Number is as follows, and wherein Δ t is the time difference of adjacent two frame.
Systematic state transfer matrix A：
Systematic observation Matrix C：
By many experiments, determine that the concrete numerical value of other relevant parameters is as follows：
Systematic procedure noise covariance matrix Q：
Wherein, e is natural logrithm；
The covariance matrix R of systematic observation noise：
Setting initial time system Kalman filter Square Error matrix P0：
Step 3, according to the target-like state value of previous moment, is predicted in advance using Kalman filter, obtains current time
Dbjective state predicted value.
Specifically, by state-transition matrix A and last moment dbjective state vector xk-1Substitute into system state equation xk=
Axk-1+vk-1In, obtain the predicted state vector x of current kinetic targetk；Then by xkSubstitute into systematic observation equation yk=Cxk+wk
In, obtain the prediction observation vector y of current kinetic targetk；The related parameter values A, Q that finally previous frame is tried to achievek-1,Pk-1Together
Substitute into predicting covariance matrix equation P'k=APk-1AT+Qk-1, to predicting covariance P'kIt is predicted.
Step 4, the target predicted position x tried to achieve with step 3kIt is the center of region of search, is obtained using Camshift algorithms
The target status information at current time is obtained, as observation position yk_c(observation).
Specifically, when carrying out object matching using Camshift algorithms, the target predicted position x tried to achieve with step 3kAs
The center in current search region, according to the H histogram of component of previous frame image target area, in current candidate target area,
According to Meanshift criterions, best match position is found, obtain target centroid point yk_c=[μ x 'k,μy′k]T。
Step 5, the predicted position y obtained according to step 3 Kalman filteringkObtained using Camshift algorithms with step 4
Observation position yk_cTo correct the Kalman filtering tentative prediction of step 3, so as to obtain the target optimal estimation at current time
Position.
Specifically, first, according to formula Kk=P'kCT·(CP'kCT+Rk)-1Calculate Kalman filter gain coefficient
Kk, then by Kalman filter gain COEFFICIENT KkWith Kalman filter observation ykAnd the sight that Camshift algorithms are obtained
Location puts yk_c, substitute into state revision EQUATION x 'k=xk+Kk(yk_c-yk), obtain by the revised shape of currently practical observation information
State vector x 'k, while correcting predicting covariance matrix P'k=APk-1AT+Qk-1With Kalman filtering Square Error matrix Pk
=(1-KkC)P'k, for the target state estimator of subsequent time provides effective information.
Step 6, Kalman's correction position x ' that step 5 is obtainedkIt is the target-like state value of present frame, as in image
Target centroid point, reads next two field picture, performs step 3~step 6, repeats prediction, matching, makeover process, so that it may realize embedding
Enter tracking of the Camshift algorithms of Kalman filter to moving target.So in the treatment of each frame, Kalman is all used
Filtering carries out state estimation to target, can effectively improve the tracking effect of target.
In sum, presently preferred embodiments of the present invention is these are only, is not intended to limit the scope of the present invention.
All any modification, equivalent substitution and improvements within the spirit and principles in the present invention, made etc., should be included in of the invention
Within protection domain.
Claims (3)
1. it is a kind of based on the Camshift machine fish trackings for being embedded in Kalman filter, it is characterised in that including following step
Suddenly：
Step 1, before target following is carried out, by man-machine interaction mode, tracking area is chosen with mouse on tracking picture manually
Domain；
Step 2, builds Kalman filter and initializes；
Wherein, dbjective state vector x=[μ x, μ y, vx, vy]T, wherein, the barycenter that μ x, μ y represent tracked target respectively is being regarded
X in the rectangular coordinate system Oxy of frequency image, the coordinate components in y-axis, vx, vy represent speed of the target in x, y-axis point respectively
Amount, i.e., every two frames target movement position represents transposition in x directions and the difference in y directions, subscript T；
Target observation vector y=[μ x, μ y]T；
Systematic state transfer matrixΔ t is the time difference of adjacent two frame；Systematic observation matrix
The process noise covariance matrix Q of system and the covariance matrix R of observation noise are respectively：
Wherein, e is natural logrithm；
Initial time Kalman filter Square Error matrix P0For：
Step 3, the dbjective state vector x at the Kalman filter prediction current k moment built using step 2kWith target observation to
Amount yk；
Step 4, with the dbjective state vector x of step 3 Kalman filter predictionkIn position for region of search center, utilize
Camshift algorithms obtain the target position information at current k moment, as Camshift observation vectors yk_c；
Step 5, according to formula Kk=P'kCT·(CP'kCT+R)-1Calculate the Kalman filter gain coefficient at current k moment
Kk, wherein, P'k=APk-1AT+ Q, Pk-1It is k-1 moment Kalman filter Square Error matrix；By Kk, step 3 Kalman filtering
The target observation vector y of device predictionkAnd the Camshift observation vectors y that step 4 is obtainedk_c, substitute into state revision EQUATION x 'k=
xk+Kk(yk_c-yk), obtain revised state vector x 'k；And according to Pk=(1-KkC)P'kUpdate Kalman filtering mean square error
Difference matrix Pk；
Step 6, the revised state vector x ' that step 5 is obtainedkAs the target-like state value of present frame, next frame figure is read
Picture, performs step 3~step 6.
2. the Camshift machine fish trackings of embedded Kalman filter are based on as claimed in claim 1, and its feature exists
In in the step 3, by state-transition matrix A and last moment dbjective state vector xk-1Substitute into system state equation xk=
Axk-1+vk-1In, predict current k moment dbjective state vector xk, wherein, vk-1It is systematic procedure noise vector；By predicted state
Vector xkSubstitute into systematic observation equation yk=Cxk+wkIn, predict current k moment target observation vector yk, wherein, wkFor observation is made an uproar
Sound vector.
3. the Camshift machine fish trackings of embedded Kalman filter are based on as claimed in claim 1, and its feature exists
In in the step 4, with the dbjective state vector x of step 3 Kalman filter predictionkIn position as the current k moment
The center of region of search, according to the H histogram of component of previous frame image target area, in current candidate target area, foundation
Meanshift criterions, find best match position, target centroid point are obtained, as Camshift observation vectors yk_c。
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