CN101955130A - Tower crane video monitoring system with automatic tracking and zooming functions and monitoring method - Google Patents
Tower crane video monitoring system with automatic tracking and zooming functions and monitoring method Download PDFInfo
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Abstract
The invention discloses a tower crane video monitoring system with automatic tracking and zooming functions, and the invention also discloses a tower crane video monitoring method with automatic tracking and zooming functions. The method comprises the following implementing steps of taking a marker on a crane hook as a tracking target image, and detecting the area and the center of mass of an object at real time by the image processing technology to realize the automatic zooming and tracking, image sequence acquisition, target characteristic extraction, target characteristic match and target search location; and aiming at swaying in the running process of the tower crane, combining a color histogram with form features, and then improving the present mean shift tracking algorithm which is based on the distribution of a target color histogram according to the real operating condition of the tower crane, thereby realizing the target tracking under the environment of the tower crane. The invention has automatic camera zooming and tracking functions so that a tower crane operator can watch the lifted objects and the circumferential situation thereof clearly in a control room at real time .
Description
Technical field
The invention belongs to observing and controlling monitoring technique field, relate to a kind of tower machine video monitoring system, the invention still further relates to a kind of tower machine video frequency monitoring method with automatic tracking anamorphosis function with automatic tracking anamorphosis function.
Background technology
Tower crane (hereinafter to be referred as the tower machine) belongs to aerial lift device, operator's compartment is positioned at the tower crane top, in the working process, tower machine operation personnel often do not see surface state, finish needed operation in order to make the tower function, generally need the ground control personnel to cooperate with tower machine operation person, the tower crane operating personal is handled hoisting crane according to commanding's instruction.Under this operating mode, on the one hand,, be easy to take place safety misadventure if the lacking a sense of responsibility of commanding, profile are not enough; On the other hand, the tower crane driver be owing to can't see working environment clearly, operation relatively blindly, spirit is relatively more nervous during work, and is tired easily and cause major accident.Therefore, the visualization problem when solution tower machine operation employee does is the effective ways that improve construction safety.
Existing tower machine video monitoring apparatus, as patent " tower crane visual field monitor unit " (patent No. ZL01239897.7, publication number CN2474505Y, a day 2000.1.30 is disclosed) and patent " tower crane visible monitoring apparatus " (patent No. ZL200520017117.7, publication number CN2813563Y, open day 2006.9.6), alleviated the problem of cooperating between commanding and the tower machine driver to a certain extent, but these automation degree of equipment are lower, in the course of the work, need tower machine operation personnel according to circumstances at any time the camera multiplying power to be carried out hand adjustment, therefore, existing tower machine video monitoring apparatus operability is bad, causes tower machine operation personnel to attend to one thing and lose sight of another maloperation in the operating process easily.
Summary of the invention
The purpose of this invention is to provide a kind of tower machine video monitoring system with automatic tracking anamorphosis function, solved and existed in the prior art, tower machine operation personnel need manually to control at any time the adjustment of camera multiplying power during work, and the operability of system is bad, and work efficiency is lower; Also attend to one thing and lose sight of another easily, cause the problem of maloperation.
Another object of the present invention provides a kind of tower machine video frequency monitoring method with automatic tracking anamorphosis function.
The technical solution adopted in the present invention is, a kind of tower machine video monitoring system with automatic tracking anamorphosis function, the control, demonstration and the recording section that comprise camera part, middle signal transporting part and the rear end of front end, camera part is installed on the amplitude variation trolley of tower machine, the camera lens of camera vertically downward, object is set on suspension hook, is used for the content that camera is monitored is converted into picture signal, and signal is sent on the telltale of control, demonstration and recording section; The signal transporting part is used for camera part is connected with control, demonstration and recording section; It is indoor that control, demonstration and recording section are installed in the tower machine operation, and camera is connected with control, demonstration and recording section by demoder, and the change that is used to control The Cloud Terrace and camera doubly and is handled in real time and shown vision signal.
Another technical scheme of the present invention is, a kind of tower machine video frequency monitoring method with automatic tracking anamorphosis function, earlier camera part is installed on the amplitude variation trolley of tower machine, the camera lens of camera vertically downward, the object of regular shape is set on suspension hook, connect camera part and control by the signal transporting part again, show and recording section, control, it is indoor that demonstration and recording section are installed in the tower machine operation, camera is by demoder and control, show and the recording section connection, adjust camera, make object clearly to be presented in the image, and selected tracking target, in tower machine operational process, change automatic compensation camera multiplying power and The Cloud Terrace angle according to object area and barycenter and realize that camera becomes doubly and tracking automatically, guarantee that object clearly is presented at control all the time, in the display image of demonstration and recording section, concrete implementation step is as follows:
Image M in the window of step 1, initialization tracking target thing will comprise whole object in the image M;
V＝max(R，G，B) (1)
When H＜0, H=H+360 then
Wherein, H represents tone, and S represents degree of saturation specific humidity, and V represents brightness; R, G, B represent red, green, blue respectively, by formula (1), (2), (3), each pixel among the target image M are transformed into the HSV space from RGB, obtain the HSV model M of tracking target image M '
HSV
The HSV model M of step 3, tracking target image M that step 2 is obtained '
HSVIn value on the H passage of each pixel sample, thereby obtain the color histogram of tracking target image M, with the color histogram model of this color histogram as tracking target image M;
The color probability distribution graph of step 5, generation current search window, each pixel of current search window statistic with respective pixel in the color histogram of tracking target image M is replaced, the re-quantization as a result that will obtain then, the scope that is about to the H component quantizes to [0,255], obtain the color probability distribution graph of current search window;
The current search video in window is converted into the color probability distribution graph, finds out the barycenter and the area of this color probability distribution graph by the Meanshift algorithm, concrete steps are as follows:
6.1) calculate the zeroth order square M of current search video in window
00:
Wherein, (x y) is image mid point (x, the pixel value of y) locating, (x, y) value in the current image window scope to I;
6.2) calculating current search video in window x axle and the axial first moment of y:
Then the coordinate of barycenter c is:
6.3) reset the big or small s of search window:
6.4) repeating step 6.1) to step 6.3), obtain next step new search window barycenter c ' (x '
c, y '
c) with the big or small s ' of new search window, in the iterative process, when centroid position changes less than given threshold epsilon, promptly ‖ c '-c ‖≤ε reaches convergence, stops iteration;
In the process of iterative computation, the size of search window constantly changes, to the last the iterative center of mass change in location just reaches convergence less than given threshold value, promptly obtain the area s and the barycenter c of tracking target object image in the current search window, to become doubly and the mobile reference of tracking as camera with this, if iteration result's search window s area increases, then control the camera multiplying power and increase; If search window s area reduces, then reduce the camera multiplying power; Simultaneously, according to the relative change direction of centroid position, adjust the The Cloud Terrace luffing synchronously;
Automatically become in times tracing process at camera, each Meanshift algorithm convergence finishes, and the camera multiplying power will be adjusted 1 times on demand, and the The Cloud Terrace pitch angle is adjusted 1 degree, changes next step then over to, carries out consecutive image and follows the tracks of;
Step 7:Camshift algorithm is to use color histogram as feature to the consecutive image sequence, all frames to the continuous videos image are done the MeanShift computing, and the search box size of utilizing every frame result changes the change of control camera doubly, obtain the next frame new images then, again carry out the MeanShift iteration, so iteration is gone down, up to the size variation of search window less than threshold values ε, thereby realize becoming doubly automatically
Adopt improved Camshift algorithm that tower machine mark is followed the tracks of, improved CamShift algorithm steps is as follows:
7.1) size and the position of initialization search window;
7.2) calculate and search for the interior color probability distribution of window;
7.3) operation Meanshift iterative algorithm, if iteration does not restrain, change step 8 over to; If iteration convergence then obtains the big or small s and the position c of search window, continue next step;
7.4) control multiplying power of camera and The Cloud Terrace motion, if search window area s increases then corresponding increase camera multiplying power, opposite search window area s reduces, then reduce the camera multiplying power, in tracing process, the camera multiplying power is that unit adjusts with each 1 times;
7.5) after camera multiplying power and The Cloud Terrace adjust, the new searching image of a frame will be obtained, and with step 7.3) the big or small s and the new search window of position c initialization of the rope window that obtains, jump to step 7.2 again) go on foot and carry out the Camshift tracing process again, in whole tracing process, thereby iteration realizes in the suspension hook motion process, the tracking of mark;
The invention has the beneficial effects as follows, tower machine video monitoring has the automatic tracking anamorphosis function, this video monitoring system lens ratio can be according to the surrounding environment automatically regulating, make tower machine operation personnel can see the situation of hanging object and periphery thereof in real time clearly by display equipment at control cabin, thereby carry out correct operation, alleviate tower machine operation personnel's labour intensity, simultaneously, effectively avoid because the grave accident that commander's error causes provides safer building operation environment.
Description of drawings
Fig. 1 is the structural representation of video monitoring system of the present invention;
Fig. 2 is the installation site scheme drawing of video monitoring system of the present invention;
Fig. 3 is existing RGB color model scheme drawing;
Fig. 4 is existing HSI color model scheme drawing;
Fig. 5 is the improved target tracking Camshift algorithm flow chart in the inventive method.
Among the figure, 1. camera part, 2. signal transporting part, 3. control, demonstration and recording section, 4. tracking target, 5. camera, 6. demoder, 7. computing machine, 8. video frequency collection card, 9. transmission medium.
The specific embodiment
The present invention is described in detail below in conjunction with the drawings and specific embodiments.
As Fig. 1, shown in Figure 2, the structure of monitored control system of the present invention is, comprise camera part 1, middle signal transporting part 2 and control, show and recording section 3, camera part 1 is installed on the amplitude variation trolley of tower machine, camera 5 wherein is with the luffing moving of car, and camera lens vertically downward, object 4 (adopting the ball-shaped mark among the present invention) is set on suspension hook, object 4 remains in the monitoring range of camera 5, control, it is indoor that demonstration and recording section 3 are installed in the tower machine operation, camera part 1 and control, connect by the transmission medium in the signal transporting part 2 between demonstration and the recording section 3, tower machine operation personnel are by control, read-out in demonstration and the recording section 3 just can be observed suspended object and surrounding condition thereof always.
Signal transporting part 2 is passages of system diagram image signal, control signal, power supply signal, is bearing the transmission work of image data stream, control signal, power supply signal, transmission medium 9 corresponding employing video lines, power lead, control line.
Control, demonstration and recording section 3 are cores of whole monitoring system, mainly comprise two parts function, the one, and the change of control The Cloud Terrace and camera 5 is doubly; The 2nd, finish the real-time Presentation Function of vision signal.Consider that space, tower machine operation chamber is narrow and small, be unsuitable for placing big more article, control, demonstration and recording section 3 have adopted the panel computer with touch function.
Tower machine video monitoring system of the present invention, utilize automatic tracking that image processing techniques finishes zoom camera 5 and become doubly, its principle of work is: the target that the object 4 that is provided with on the suspension hook is followed the tracks of as monitored control system, when the tower machine hoists or descending motion, the area of object 4 can change in the graphicinformation of camera 5, according to these characteristics, system passes through image processing algorithm, calculate the variation of object 4 areas in real time, automatically become foundation doubly as control camera 5, realize automatic tracking, thereby guarantee tower machine operation personnel at work, visual effect is best.Quick and effective for what guarantee to follow the tracks of, in control of the present invention, demonstration and the recording section 3, adopted improved Camshift algorithm, color is combined as tracking target with area; Simultaneously, under special circumstances with the mark shape as tracking target, thereby improve to follow the tracks of efficient;
The inventive method is improved existing C amshift algorithm, the principle of work of the Camshift algorithm after the improvement is: the iconic model of at first determining tracking target object 4, specify first frame search the window's position and the size then, barycenter and area with object 4 in the Meanshift algorithm keeps track search window, if algorithm convergence then utilize result of calculation to adjust the camera multiplying power, and control The Cloud Terrace, simultaneously, utilize new search window position and size in the result of calculation initialization next frame image, and start new round Camshift algorithm; If algorithm is not restrained, then judge by shape facility whether this object is the tracking target shape, if then continue to follow the tracks of, get back to camera 5 belows and wait for that object 4 occurs once more if not then abandoning following the tracks of.
The concrete implementation step of the inventive method is:
At first adjust camera 5, make object 4 clearly to be presented in the image, and selected tracking target, in tower machine operational process, change automatic compensation camera multiplying power and The Cloud Terrace angle according to object 4 areas and barycenter and realize that camera becomes doubly and tracking automatically, guarantee that object 4 clearly is presented in the image all the time, the specific implementation step is as follows:
Image M in the window of step 1, initialization tracking target thing 4 will comprise whole object 4;
(1) RGB model
The RGB color space is by red (R), and green (G), blue (B) three kinds of colors are formed, and it is the most frequently used a kind of color model of hardware device, is used for the colour display system of vision receiver and computing machine more.By red, green, blue three kinds of mix of basic colors obtain most color.What the RGB color space adopted is Cartesian coordinates, and three axles are respectively R, G, B.Initial point then drops in the color cube of being made up of red-green-blue in the three dimensional space, as shown in Figure 3 corresponding to other color of white from initial point summit farthest corresponding to black, diagonal line arrives (1 from (0,0,0), 1,1) representative is to change to the lime degree from black.
(2) HSV model
HSV model (Hue saturation value) model is a kind of color matching model corresponding to the artist, can better reflect perception and the discriminating of people to color, the three elements (form and aspect, lightness and degree of saturation specific humidity) of the direct corresponding human eye color vision feature in HSV space, three components are independent mutually.As shown in Figure 4, the HSV system of axes adopts is that circular cylindrical coordinate and all definitions of color are in hexagonal pyramid.Tone (Hue) is decided by dominant wavelength in the object reflected light rays, and different wavelength produces different color perceptions.It is not influenced by bright light, the light and shade of color, and its span is 0 to 360; The in bright gay color light degree of degree of saturation specific humidity (Saturation) expression, the ratio of white of promptly mixing in the color of same form and aspect if wherein not having white mixes, is called pure color, and the low more then color of white ratio is distinct more, otherwise, will become light more.Its span is from 0 to 1, obtains the purest color (being not white) when S=1; The GTG degree of brightness (Value) expression color shade is as the power of tone color.Its value also is from 0 to 1, obtains black when getting 0 value.
Though the RGB color space is that the correlativity of three components of RGB is very big in the RGB color space towards the most frequently used a kind of color space of hardware device, that is to say at R, G, when certain component of B changed, other components also can and then change.The variation of Illumination intensity also has bigger influence to the value of RGB component in addition, and this has just brought very big inconvenience to practical application.Comparatively speaking, hsv color space shown in Figure 4 more meets human vision property than RGB color space, and three components of HSV are independently, can consider separately that calculated amount is littler than three components of RGB color space.Therefore with current video image from the RGB color space conversion to the hsv color space.
Transfer algorithm from the GRB color space to the hsv color space has a lot, although they are different on arithmetic speed, complexity, but the H component that obtains at last is similar substantially with the S component image, and the present invention adopts following formula that the color space of pixel is transformed into the HSV space from RGB:
V＝max(R，G，B) (1)
When H＜0, H=H+360 then
Wherein, H represents tone, and S represents degree of saturation specific humidity, and V represents brightness; R, G, B represent red, green, blue respectively.By formula (1), (2), (3), each pixel among the target image M is transformed into the HSV space from RGB, obtain the HSV model M of tracking target image M '
HSV
The HSV model M of step 3, tracking target image M that step 2 is obtained '
HSVIn value on the H passage of each pixel sample, thereby obtain the color histogram of tracking target image M, this color histogram is preserved the color histogram model that is used as tracking target image M down.
Color histogram has characterized the distribution frequency of color of image, describes the integral color feature of image with color histogram.Suppose that for tracking target image M size is I
1* I
2, each component value is C
k(C
k=1,2 ..., n), define the statistical value h[C of one group of pixel
1], h[C
2] ..., h[C
n] be the color frequency of this image:
H[C wherein
k] be color value C
kAppearance frequency in image, and T (M[i, j]) be pixel (i, j) color value in color space.With the color progression in the image is abscissa, and the color frequency of occurrences is the color histogram that the figure of ordinate just is called image.
Need consider the problem of the span of H component at this, the span of H component is [0,360], the value of this span can not be represented with a byte, in order to represent that the present invention does suitable quantification treatment with the H value with a byte, the scope of H component is quantized to [0,255].
Step 5: the color probability distribution graph that generates the current search window.Use the statistic of respective pixel in the color histogram of tracking target image to replace each pixel of current search window, the re-quantization as a result that will obtain then, the scope that is about to the H component is quantized to [0,255], just obtains the color probability distribution graph of current search window.In order to reduce the expense of time, among the present invention as probability 0 zone with extra-regional other zones of image processing.
Step 6: utilize the Meanshift iterative algorithm, obtain in the current search window area of tracking target color and barycenter.
After the current search video in window is converted into the color probability distribution graph, the Meanshift algorithm will be found out the barycenter and the area of this color probability distribution graph, and calculation procedure is as follows:
(6.1) the zeroth order square M of calculating current search video in window
00:
Wherein, (x y) is image mid point (x, the pixel value of y) locating, (x, y) value in the current image window scope to I;
(6.2) calculate current search video in window x axle and the axial first moment of y:
Then the coordinate of barycenter c is:
(6.3) reset the big or small s of search window:
(6.4) repeat (6.1) step to (6.3) step, obtain next step new search window barycenter c ' (x '
c, y '
c) with the big or small s ' of new search window, in the iterative process, when centroid position changes less than given threshold epsilon, be ‖ c '-c ‖≤ε, reach convergence, stop iteration, wherein the value of ε depends on the needed precision of engineering, threshold epsilon depends on follows the tracks of desired precision, in tower machine automatic tracking zooming system, gets 5-8 pixel.
In the process of iterative computation, the size of search window constantly changes, to the last the iterative center of mass change in location just reaches convergence less than given threshold value, promptly obtain the area s and the barycenter c of tracking target object image in the current search window, to become doubly and the mobile reference of tracking as camera with this, if iteration result's search window s area increases, then control the camera multiplying power and increase; If search window s area reduces, then reduce the camera multiplying power; Simultaneously, according to the relative change direction of centroid position, adjust the The Cloud Terrace luffing synchronously;
Automatically become in times tracing process at camera, the luffing of adjustment of camera multiplying power and The Cloud Terrace is that unit carries out (precision and rate of following that this step-length value depends on tracking) with step-length mouth c, each Meanshift algorithm convergence finishes, adjust camera multiplying power c and The Cloud Terrace pitch angle, the camera multiplying power will be adjusted 1 times on demand, the The Cloud Terrace pitch angle is adjusted 1 degree, changes next step then over to, carries out consecutive image and follows the tracks of;
Step 7:Camshift algorithm is to use color histogram as feature to the consecutive image sequence, all frames to the continuous videos image are done the MeanShift computing, and the search box size of utilizing every frame result changes the change of control camera doubly, obtain the next frame new images then, again carry out the MeanShift iteration, so iteration is gone down, up to the size variation of search window less than threshold values ε, thereby realize becoming doubly automatically.
Among the present invention, adopt improved Camshift algorithm that tower machine mark is followed the tracks of; Because tower machine building site circumstance complication inevitably has the building site article color situation identical with the characteristics of image color and occurs, therefore, the improvement track algorithm that the present invention adopts shape and Camshift algorithm to combine, improved CamShift algorithm steps is as follows:
(7.1) size of initialization search window and position;
(7.2) calculate the interior color probability distribution of search window;
(7.3) operation Meanshift iterative algorithm if iteration does not restrain, changes step 8 over to; If iteration convergence then obtains the big or small s and the position c of search window, continue next step;
(7.4) multiplying power of control camera 5 and The Cloud Terrace motion, if search window area s increases, the then corresponding camera multiplying power that reduces, opposite search window area s reduces, and then increases the camera multiplying power.In tracing process, the adjustment of camera multiplying power is that unit carries out with 1 times;
(7.5) after camera multiplying power and The Cloud Terrace are adjusted, will obtain the new searching image of a frame, and the big or small s and the new search window of position c initialization of the rope window that obtains with (7.3) step, jumping to for (7.2) step again carries out the Camshift tracing process again.In whole tracing process, thereby iteration realizes in the suspension hook motion process tracking of mark.
Step 8: when the iterations of search window or relative distance changes or window size changes and surpasses certain threshold value, illustrate that Camshift follows the tracks of calculating and goes wrong, then extract the profile of current mobile, carrying out shape judges, judge that the mobile shape weighs mobile again and whether disappear, if moving target consistent with the blip thing (being circular in this algorithm) then continues to follow the tracks of; If it is inconsistent, then allow search window get back to the most initial position of whole track algorithm, be camera 5 belows, restart the Camshift track algorithm, wait for that the circular target mark occurs, the Meanshift iterative algorithm can be restrained, and restarts to control camera multiplying power and The Cloud Terrace motion, enters the multiplying power adjustment and the tracking of a new round.
Claims (5)
1. tower machine video monitoring system with automatic tracking anamorphosis function, it is characterized in that: control, demonstration and the recording section (3) that comprise camera part (1), middle signal transporting part (2) and the rear end of front end, camera part (1) is installed on the amplitude variation trolley of tower machine, the camera lens of camera (5) vertically downward, object (4) is set on suspension hook, be used for the content that camera (5) is monitored is converted into picture signal, and signal be sent on the telltale of control, demonstration and recording section (3); Signal transporting part (2) is used for camera part (1) is connected with control, demonstration and recording section (3); It is indoor that control, demonstration and recording section (3) are installed in the tower machine operation, and camera (5) is connected with control, demonstration and recording section (3) by demoder (6), the change that is used to control The Cloud Terrace and camera (5) doubly, and to vision signal processing in real time and demonstration.
2. the tower machine video monitoring system with automatic tracking anamorphosis function according to claim 1, it is characterized in that: described camera (5) disposes protective cover, support and The Cloud Terrace.
3. tower machine video frequency monitoring method with automatic tracking anamorphosis function, it is characterized in that, earlier camera part (1) is installed on the amplitude variation trolley of tower machine, the camera lens of camera (5) vertically downward, the object (4) of regular shape is set on suspension hook, connect camera part (1) and control by signal transporting part (2) again, show and recording section (3), control, it is indoor that demonstration and recording section (3) are installed in the tower machine operation, camera (5) is by demoder (6) and control, show and recording section (3) connection, adjust camera (5), make object (4) clearly to be presented in the image, and selected tracking target, in tower machine operational process, change automatic compensation camera multiplying power and The Cloud Terrace angle according to object (4) area and barycenter and realize that camera becomes doubly and tracking automatically, guarantee that object (4) clearly is presented at control all the time, in the display image of demonstration and recording section (3), concrete implementation step is as follows:
Image M in the window of step 1, initialization tracking target thing (4) will comprise whole object (4) in the image M;
Step 2, extract tracking target image M, and with target image M according to following formula with the color space of pixel from the RGB color space conversion to the hsv color space, obtain tracking target image M '
HSV:
V＝max(R，G，B) (1)
When H＜0, H=H+360 then
Wherein, H represents tone, and S represents degree of saturation specific humidity, and V represents brightness; R, G, B represent red, green, blue respectively, by formula (1), (2), (3), each pixel among the target image M are transformed into the HSV space from RGB, obtain the HSV model M of tracking target image M '
HSV
The HSV model M of step 3, tracking target image M that step 2 is obtained '
HSVIn value on the H passage of each pixel sample, thereby obtain the color histogram of tracking target image M, with the color histogram model of this color histogram as tracking target image M;
Step 4, initialization current search position of window and size, current search window will comprise the object (4) of whole tracking target;
The color probability distribution graph of step 5, generation current search window, each pixel of current search window statistic with respective pixel in the color histogram of tracking target image M is replaced, the re-quantization as a result that will obtain then, the scope that is about to the H component quantizes to [0,255], obtain the color probability distribution graph of current search window;
Step 6, utilize the Meanshift iterative algorithm, obtain in the current search window area and the barycenter of institute's tracking target color:
The current search video in window is converted into the color probability distribution graph, finds out the barycenter and the area of this color probability distribution graph by the Meanshift algorithm, concrete steps are as follows:
6.1) calculate the zeroth order square M of current search video in window
00:
Wherein, (x y) is image mid point (x, the pixel value of y) locating, (x, y) value in the current image window scope to I;
6.2) calculating current search video in window x axle and the axial first moment of y:
Then the coordinate of barycenter c is:
6.3) reset the big or small s of search window:
6.4) repeating step 6.1) to step 6.3), obtain next step new search window barycenter c ' (x '
c, y '
c) with the big or small s ' of new search window, in the iterative process, when centroid position changes less than given threshold epsilon, promptly ‖ c '-c ‖≤ε reaches convergence, stops iteration;
In the process of iterative computation, the size of search window constantly changes, to the last the iterative center of mass change in location just reaches convergence less than given threshold value, promptly obtain the area s and the barycenter c of tracking target object image in the current search window, to become doubly and the mobile reference of tracking as camera with this, if iteration result's search window s area increases, then control the camera multiplying power and increase; If search window s area reduces, then reduce the camera multiplying power; Simultaneously, according to the relative change direction of centroid position, adjust the The Cloud Terrace luffing synchronously;
Automatically become in times tracing process at camera, each Meanshift algorithm convergence finishes, and the camera multiplying power will be adjusted 1 times on demand, and the The Cloud Terrace pitch angle is adjusted 1 degree, changes next step then over to, carries out consecutive image and follows the tracks of;
Step 7:Camshift algorithm is to use color histogram as feature to the consecutive image sequence, all frames to the continuous videos image are done the MeanShift computing, and the search box size of utilizing every frame result changes the change of control camera doubly, obtain the next frame new images then, again carry out the MeanShift iteration, so iteration is gone down, up to the size variation of search window less than threshold values ε, thereby realize becoming doubly automatically
Adopt improved Camshift algorithm that tower machine mark is followed the tracks of, improved CamShift algorithm steps is as follows:
7.1) size and the position of initialization search window;
7.2) calculate and search for the interior color probability distribution of window;
7.3) operation Meanshift iterative algorithm, if iteration does not restrain, change step 8 over to; If iteration convergence then obtains the big or small s and the position c of search window, continue next step;
7.4) multiplying power and the The Cloud Terrace motion of control camera (5), if search window area s increases then corresponding increase camera multiplying power, opposite search window area s reduces, then reduce the camera multiplying power, in tracing process, the camera multiplying power is that unit adjusts with each 1 times;
7.5) after camera multiplying power and The Cloud Terrace adjust, the new searching image of a frame will be obtained, and with step 7.3) the big or small s and the new search window of position c initialization of the rope window that obtains, jump to step 7.2 again) go on foot and carry out the Camshift tracing process again, in whole tracing process, thereby iteration realizes in the suspension hook motion process, the tracking of mark;
Step 8, when the iterations of search window or relative distance changes or window size changes and surpasses certain threshold value, then extract the profile of current mobile, carrying out shape judges, judge that the mobile shape weighs mobile again and whether disappear, if moving target is consistent with blip thing shape, then continue to follow the tracks of; If it is inconsistent, then make search window get back to the most initial position of whole track algorithm, it is camera (5) below, restart the Camshift track algorithm, the object (4) of waiting for set shape occurs, the convergence of Meanshift iterative algorithm then restarts to control the motion of camera multiplying power and The Cloud Terrace, enters the multiplying power adjustment and the tracking of a new round.
4. the tower machine video frequency monitoring method with automatic tracking anamorphosis function according to claim 3 is characterized in that, in step 6.4) in, threshold epsilon depends on follows the tracks of desired precision, in tower machine automatic tracking zooming system, gets 5-8 pixel.
5. the tower machine video frequency monitoring method with automatic tracking anamorphosis function according to claim 3, it is characterized in that, in step 8, when the iterations of search window or relative distance changes or window size changes and surpasses certain threshold value, promptly get iterations 20 times, the window variable in distance is greater than 200 pixels, and window size changes above 50%.
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| CN2010102760805A CN101955130B (en) | 2010-09-08 | 2010-09-08 | Tower crane video monitoring system with automatic tracking and zooming functions and monitoring method |
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| CN102530727A (en) * | 2012-02-02 | 2012-07-04 | 上海成业科技工程有限公司 | Lifting appliance shot automatic tracking and control system |
| CN103024355A (en) * | 2012-12-12 | 2013-04-03 | 上海市安装工程有限公司 | Video monitoring and walkie-talkie network system used for high-rise building construction |
| CN103264969A (en) * | 2013-06-03 | 2013-08-28 | 中联重科股份有限公司 | Equipment, system and method for safely controlling tower crane, and tower crane |
| CN103613013A (en) * | 2013-11-12 | 2014-03-05 | 宁夏电通物联网科技有限公司 | System and method for monitoring construction safety of tower crane |
| CN104092991A (en) * | 2014-07-11 | 2014-10-08 | 金陵科技学院 | Image signal comprehensive processing device and implementation method thereof according to target tracking control |
| CN104854017A (en) * | 2012-12-17 | 2015-08-19 | 比伯拉赫利勃海尔-部件股份有限公司 | Rotating tower crane |
| TWI497450B (en) * | 2013-10-28 | 2015-08-21 | Univ Ming Chuan | Visual object tracking method |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2474505Y (en) * | 2001-04-28 | 2002-01-30 | 张恩保 | Visual expansion device of tower machine |
| CN1449186A (en) * | 2003-04-03 | 2003-10-15 | 上海交通大学 | Abnormal object automatic finding and tracking video camera system |
| CN1591071A (en) * | 2003-08-29 | 2005-03-09 | 京瓷株式会社 | Zoom pick-up lens and zoom pick-up device |
| CN2766472Y (en) * | 2005-02-21 | 2006-03-22 | 上海大屯能源股份有限公司 | Pan unit controller in industrial television monitoring system |
| CN2813563Y (en) * | 2005-04-20 | 2006-09-06 | 苗小平 | Tower crane visual monitoring device |
| CN201002925Y (en) * | 2006-09-11 | 2008-01-09 | 何军 | Wireless image pick-up monitoring apparatus for tower machine |
| CN201228202Y (en) * | 2008-06-11 | 2009-04-29 | 郑州市三力实业有限公司 | Recording device for monitoring crane safe operation |
-
2010
- 2010-09-08 CN CN2010102760805A patent/CN101955130B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2474505Y (en) * | 2001-04-28 | 2002-01-30 | 张恩保 | Visual expansion device of tower machine |
| CN1449186A (en) * | 2003-04-03 | 2003-10-15 | 上海交通大学 | Abnormal object automatic finding and tracking video camera system |
| CN1591071A (en) * | 2003-08-29 | 2005-03-09 | 京瓷株式会社 | Zoom pick-up lens and zoom pick-up device |
| CN2766472Y (en) * | 2005-02-21 | 2006-03-22 | 上海大屯能源股份有限公司 | Pan unit controller in industrial television monitoring system |
| CN2813563Y (en) * | 2005-04-20 | 2006-09-06 | 苗小平 | Tower crane visual monitoring device |
| CN201002925Y (en) * | 2006-09-11 | 2008-01-09 | 何军 | Wireless image pick-up monitoring apparatus for tower machine |
| CN201228202Y (en) * | 2008-06-11 | 2009-04-29 | 郑州市三力实业有限公司 | Recording device for monitoring crane safe operation |
Cited By (34)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102306384B (en) * | 2011-08-26 | 2013-04-10 | 华南理工大学 | Color constancy processing method based on single image |
| CN102306384A (en) * | 2011-08-26 | 2012-01-04 | 华南理工大学 | Color constancy processing method based on single image |
| CN102530727A (en) * | 2012-02-02 | 2012-07-04 | 上海成业科技工程有限公司 | Lifting appliance shot automatic tracking and control system |
| CN102530727B (en) * | 2012-02-02 | 2014-05-21 | 上海成业科技工程有限公司 | Lifting appliance shot automatic tracking and control system |
| CN103024355A (en) * | 2012-12-12 | 2013-04-03 | 上海市安装工程有限公司 | Video monitoring and walkie-talkie network system used for high-rise building construction |
| CN103024355B (en) * | 2012-12-12 | 2015-05-20 | 上海市安装工程集团有限公司 | Video monitoring and walkie-talkie network system used for high-rise building construction |
| CN104854017A (en) * | 2012-12-17 | 2015-08-19 | 比伯拉赫利勃海尔-部件股份有限公司 | Rotating tower crane |
| US9738493B2 (en) | 2012-12-17 | 2017-08-22 | Liebherr-Components Biberach Gmbh | Tower slewing crane |
| CN103264969A (en) * | 2013-06-03 | 2013-08-28 | 中联重科股份有限公司 | Equipment, system and method for safely controlling tower crane, and tower crane |
| CN103264969B (en) * | 2013-06-03 | 2014-12-10 | 中联重科股份有限公司 | Equipment, system and method for safely controlling tower crane, and tower crane |
| TWI497450B (en) * | 2013-10-28 | 2015-08-21 | Univ Ming Chuan | Visual object tracking method |
| CN103613013A (en) * | 2013-11-12 | 2014-03-05 | 宁夏电通物联网科技有限公司 | System and method for monitoring construction safety of tower crane |
| CN103613013B (en) * | 2013-11-12 | 2015-04-08 | 宁夏电通物联网科技有限公司 | System and method for monitoring construction safety of tower crane |
| WO2015154508A1 (en) * | 2014-04-11 | 2015-10-15 | 中联重科股份有限公司 | Tower crane video monitoring control apparatus, method, system, and tower crane |
| CN104092991A (en) * | 2014-07-11 | 2014-10-08 | 金陵科技学院 | Image signal comprehensive processing device and implementation method thereof according to target tracking control |
| CN105460785A (en) * | 2014-10-22 | 2016-04-06 | 徐州重型机械有限公司 | Control method and system for video monitoring and crane |
| CN105460785B (en) * | 2014-10-22 | 2019-03-19 | 徐州重型机械有限公司 | The control method and system and crane of video monitoring |
| CN106144900A (en) * | 2015-04-10 | 2016-11-23 | 宝山钢铁股份有限公司 | Automatically Heave Here suspension hook state device and method is obtained in driving driver's cabin |
| CN104986676A (en) * | 2015-07-27 | 2015-10-21 | 山东华辉自动化设备有限公司 | Tower crane comprehensive intelligent monitoring device |
| CN104994358A (en) * | 2015-07-28 | 2015-10-21 | 山东华辉自动化设备有限公司 | Pedal type tower crane video monitoring device |
| CN105427346A (en) * | 2015-12-01 | 2016-03-23 | 中国农业大学 | Motion target tracking method and system |
| CN105427346B (en) * | 2015-12-01 | 2018-06-29 | 中国农业大学 | A kind of motion target tracking method and system |
| CN106101534A (en) * | 2016-06-17 | 2016-11-09 | 上海理工大学 | Curricula based on color characteristic records the control method from motion tracking photographic head and photographic head |
| CN107344696A (en) * | 2017-08-25 | 2017-11-14 | 上海沪东集装箱码头有限公司 | Tire crane anti-collision based on real time video image identification hits early warning system and its method |
| CN107344696B (en) * | 2017-08-25 | 2018-11-02 | 上海沪东集装箱码头有限公司 | Tire crane anti-collision based on real time video image identification hits early warning system and its method |
| CN108540738A (en) * | 2018-03-19 | 2018-09-14 | 王英华 | Cloud computing positioning system |
| CN108540738B (en) * | 2018-03-19 | 2019-02-15 | 深圳耀德数据服务有限公司 | Cloud computing positioning system |
| CN110631480A (en) * | 2019-10-28 | 2019-12-31 | 苏州天准科技股份有限公司 | Manual zoom lens with electronic feedback device based on color sensor |
| CN110974284A (en) * | 2019-10-29 | 2020-04-10 | 神经元信息技术(深圳)有限公司 | X-ray protection equipment with image recognition target tracking function |
| CN112299252A (en) * | 2020-09-25 | 2021-02-02 | 山东中建众力设备租赁有限公司 | Unmanned tower crane monitoring system and monitoring method |
| CN113452913A (en) * | 2021-06-28 | 2021-09-28 | 北京宙心科技有限公司 | Zooming system and method |
| CN113452913B (en) * | 2021-06-28 | 2022-05-27 | 北京宙心科技有限公司 | Zooming system and method |
| CN117278858A (en) * | 2023-11-22 | 2023-12-22 | 杭州海康威视数字技术股份有限公司 | Target monitoring method and device |
| CN117278858B (en) * | 2023-11-22 | 2024-02-09 | 杭州海康威视数字技术股份有限公司 | Target monitoring method and device |
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Application publication date: 20110126 Assignee: Guangxi double crane manufacturing Co., Ltd. Assignor: Xi'an University of Technology Contract record no.: 2013450000061 Denomination of invention: Tower crane video monitoring system with automatic tracking and zooming functions and monitoring method Granted publication date: 20120307 License type: Exclusive License Record date: 20130722 |
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