CN105447495A - Automatic tracking apparatus realizing rapid tracking - Google Patents
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Abstract
The invention discloses an automatic tracking apparatus realizing rapid tracking. The apparatus comprises a common automatic tracking device and an object identification device installed on the automatic tracking device, wherein the identification device comprises a modeling module, a segmentation module, a merging module and a filtering module. According to the invention, through additional arrangement of the object identification apparatus on the automatic tracking device, the tracking capability of the automatic tracking device can be effectively enhanced, the automatic tracking device identifies an object through an object contour, object contour noise can be effectively filtered in the identification process, the type of the object is correctly identified, and the object is further tracked.
Description
Technical Field
The invention relates to the field of automatic tracking devices, in particular to an automatic tracking device capable of quickly tracking.
Background
The automatic tracking can provide space positioning, posture, structural behavior and performance of a moving target, and along with the progress of society and scientific technology, the automatic tracking device is applied to more and more fields. However, the current automatic tracking device is seriously interfered by noise, cannot capture a target quickly, and has low target recognition speed, so that the tracking effect is not ideal.
The target contour recognition is an important means of target recognition, and because the target contour is influenced by factors such as noise, quantization error and the like in practical application, the target contour inevitably generates distortion, and filtering and smoothing processing of the target contour is necessary to accurately describe contour characteristics. At present, scholars propose a plurality of filtering smoothing algorithms of noisy contours, but the problems of huge calculation amount, non-ideal noise reduction effect, target distortion caused by excessive filtering and the like are generally existed.
Disclosure of Invention
In order to solve the problems, the invention provides an automatic tracking device for fast tracking.
The purpose of the invention is realized by adopting the following technical scheme:
an automatic tracking device for fast tracking comprises a common automatic tracking device and a target recognition device arranged on the automatic tracking device, wherein the automatic tracking device has strong tracking capability, and the target recognition device can recognize a target according to a target contour;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); selecting a window function W (n) with width D for curvature kN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing, and determining the curvature k 'of the noisy contour according to the comparison result'N(t), namely: when | k |1N(t)-k2N(t)|>T1K'N(t)＝k1N(t) otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the feature function f (t) is 0, otherwise, the feature function f (t) is 1.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0Stopping, wherein S is a preset minimum length,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,is represented by the above windowThe obtained average curvature radius of the O point and the real-time curvature correction coefficient mu0The method is used for automatically correcting the extension length according to different curvatures of different points, and can effectively reduce the distortion phenomenon after combination; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × muO+1Or S × muO-1Is stopped at a time of muO+1And muO-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; adjacent areas of the same type are combined to obtain a continuous characteristic area and a non-characteristic area;
the filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Are respectively provided withIs that the mean is zero and the variance is sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, in order to raise the effect of suppressing noise Where σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
According to the invention, the target recognition device is additionally arranged on the automatic tracking device, so that the tracking capability of the automatic tracking device can be effectively enhanced, the automatic tracking device recognizes the target through the target profile, and the target profile noise can be effectively filtered in the recognition process, so that the target type is correctly recognized, and the target is further tracked.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a block diagram showing the structure of an automatic tracking apparatus for fast tracking according to the present invention.
Detailed Description
The invention is further described with reference to the following examples.
FIG. 1 is a block diagram of the present invention, which includes: the device comprises a modeling module, a segmentation module, a combination module and a filtering module.
Example 1: an automatic tracking device for fast tracking comprises a common automatic tracking device and a target recognition device arranged on the automatic tracking device, wherein the automatic tracking device has strong tracking capability, and the target recognition device can recognize a target according to a target contour;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the contour information, and in order to obtain an accurate curvature, a window function W (n) having a width D ∈ {7, 9} is selected, and a curvature k is calculatedN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two values to 0.24, and determining the curvature k 'of the noisy contour according to the comparison result'N(t), namely: when | k1N (T) -k2N (T) | > T1K'N(t)＝k1N(t) otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the feature function f (t) is 0, otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0Where S is a preset minimum length, in this embodiment, S is 17,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, wherein the part with large curvature needs smaller length, and the part with small curvature needs larger length, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × muO+1Or S × muO-1Is stopped at the momentμO+1And muO-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; and combining adjacent regions of the same type to obtain continuous characteristic regions and non-characteristic regions.
The filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
In this embodiment, S is 17, threshold T1The automatic tracking device has the advantages that the window function width D ∈ {7, 9}, the smoothing effect on the noisy image with the noise intensity I ∈ {10dB, 20dB } is good, the automatic tracking device identifies the target through the target contour, and the target contour noise can be effectively filtered in the identification processThe recognition speed of the target is improved by 0.2s, the recognition rate is improved by 10%, and the tracking process is stable.
Example 2: an automatic tracking device for fast tracking comprises a common automatic tracking device and a target recognition device arranged on the automatic tracking device, wherein the automatic tracking device has strong tracking capability, and the target recognition device can recognize a target according to a target contour;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the contour information, and in order to obtain an accurate curvature, a window function W (n) having a width D ∈ {10, 12} is selected, and a curvature k is calculatedN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two values to 0.24, and determining the curvature k 'of the noisy contour according to the comparison result'N(t), namely: when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t) otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the feature function f (t) is 0, otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0A stop, where S is a preset minimum length, in this embodiment S19,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0For automatically correcting the extended length according to the curvature difference of different points, the place with large curvature needs smaller length and the place with small curvatureThe required length is larger, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × muO+1Or S × muO-1Is stopped at a time of muO+1And muO-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; and combining adjacent regions of the same type to obtain continuous characteristic regions and non-characteristic regions.
The filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve better smoothing effect, one of the minimum lengths S of each type of area is selectedAnd half as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters can be adapted according to the lengths of the two types of regions.
In this embodiment, S is 19, threshold T1The automatic tracking device has the advantages that the window function width D ∈ {10, 12}, the smooth effect on noise-containing images with the noise intensity I ∈ {20dB, 30dB } is good, the automatic tracking device identifies the target through the target contour, the target contour noise can be effectively filtered in the identification process, the identification speed of the target is increased by 0.25s, the identification rate is increased by 9%, and the tracking process is stable.
Example 3: an automatic tracking device for fast tracking comprises a common automatic tracking device and a target recognition device arranged on the automatic tracking device, wherein the automatic tracking device has strong tracking capability, and the target recognition device can recognize a target according to a target contour;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the profile information, and to obtain an accurate curvature, a window of width D ∈ {13, 14} is selectedFunction W (n) for curvature kN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two parameters to 0.26, and determining the curvature k 'of the profile containing noise according to the comparison result'N(t), namely: when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t) otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the feature function f (t) is 0, otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0A time stop, where S is a preset minimum length, in this embodiment S21,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, wherein the part with large curvature needs smaller length, and the part with small curvature needs larger length, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × muO+1Or S × muO-1Is stopped at a time of muO+1And muO-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; and combining adjacent regions of the same type to obtain continuous characteristic regions and non-characteristic regions.
The filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); assuming additive noise asWhite gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
In this embodiment, S is 21, threshold T1The method has the advantages that the window function width D ∈ {13, 14}, the smoothing effect on noisy images with the noise intensity I ∈ {30dB, 40dB } is good, the calculated amount and the detail information retention condition are within an acceptable range and are well balanced, the automatic tracking device identifies the target through the target contour, the contour noise of the target can be effectively filtered in the identification process, the identification speed of the target is improved by 0.3s, the identification rate is improved by 8%, and the tracking process is stable.
Example 4: an automatic tracking device for fast tracking comprises a common automatic tracking device and a target recognition device arranged on the automatic tracking device, wherein the automatic tracking device has strong tracking capability, and the target recognition device can recognize a target according to a target contour;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the contour information, and in order to obtain an accurate curvature, a window function W (n) having a width D ∈ {15, 17} is selected, and a curvature k is calculatedN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two values to 0.28, and determining the curvature k 'of the noisy contour according to the comparison result'N(t), namely: when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t) otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the feature function f (t) is 0, otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
A merging module: the method is used for eliminating the pseudo feature points generated by noise interference and carrying out merging operation on the feature points and the non-feature points which cannot form a continuous area, so that an effective feature area and a non-feature area are obtained: select oneStarting point O, extending the outline starting point to two sides, merging the adjacent points, taking the starting point type as the preset type of the area, and extending each S × mu to two sides0A stop, where S is a preset minimum length, in this embodiment S23,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, wherein the part with large curvature needs smaller length, and the part with small curvature needs larger length, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × muO+1Or S × muO-1Is stopped at a time of muO+1And muO-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; and combining adjacent regions of the same type to obtain continuous characteristic regions and non-characteristic regions.
A filtering module: multiplicative noise varies with the change of image signal due to correlation with image signal, using wienerFiltering to perform one-stage filtering, wherein the image information also contains residual multiplicative noise, and F (x, y) is changed to q × exp (- (x) by F filter2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
In this embodiment, S-23, threshold T1The method has the advantages that 0.28 is adopted, the window function width D ∈ {15, 17}, although partial calculated amount is increased for the noisy image with the noise intensity I ∈ {40dB, 50dB }, the image in the interval has excellent smoothing effect, the detail information is better kept, the automatic tracking device identifies the target through the target contour, the target contour noise can be effectively filtered in the identification process, the identification speed of the target is improved by 0.3s, the identification rate is improved by 9%, and the tracking process is stable.
Example 5: an automatic tracking device for fast tracking comprises a common automatic tracking device and a target recognition device arranged on the automatic tracking device, wherein the automatic tracking device has strong tracking capability, and the target recognition device can recognize a target according to a target contour;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the contour information, and in order to obtain an accurate curvature, a window function W (n) having a width D ∈ {17, 19} is selected, and a curvature k is calculatedN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two parameters to 0.26, and determining the curvature k 'of the profile containing noise according to the comparison result'N(t), namely: when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t) otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the feature function f (t) is 0, otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0A stop, where S is a preset minimum length, in this embodiment 25,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, wherein the part with large curvature needs smaller length, and the part with small curvature needs larger length, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × muO+1Or S × muO-1Is stopped at a time of muO+1And muO-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; and combining adjacent regions of the same type to obtain continuous characteristic regions and non-characteristic regions.
The filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
In this embodiment, S is 25, threshold T1The automatic tracking device has the advantages that the window function width D ∈ {17, 19}, the smoothing effect on the noisy image with the noise intensity I ∈ {50dB, 60dB } is good, the detail information retention situation is good, the automatic tracking device identifies the target through the target contour, the contour noise of the target can be effectively filtered in the identification process, the identification speed of the target is increased by 0.1s, the identification rate is increased by 10%, and the tracking process is stableAnd (4) determining.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Data emulation
The automatic tracking device has the beneficial effects that: aiming at the diversity of noise types and the singleness of the current denoising method, a novel multi-time filtering device is adopted, and a novel contour segmentation and combination means and a filtering function are provided; the calculated amount is relatively uncomplicated, the factors of global characteristics and local characteristics are considered, and the smooth denoising effect is good; the difference of the contour among different types of regions is considered, and good balance is achieved between noise suppression and detail retention; according to the different curvatures of different points, the extension length is correspondingly and automatically changed in an adaptive manner, and the distortion phenomenon after combination is effectively reduced.
Through simulation, the device is compared with other devices under the noise intensity N, and the recognition rate of the target is as follows:
| intensity of noise | 0.03 | 0.06 | 0.09 | 0.12 |
| Example 1 of the apparatus | 94％ | 93％ | 92％ | 90％ |
| Example 2 of the apparatus | 93％ | 94％ | 92％ | 90％ |
| Example 3 of the apparatus | 92％ | 91％ | 93％ | 90％ |
| Example 4 of the apparatus | 91％ | 90％ | 91％ | 92％ |
| Example 5 of the apparatus | 90％ | 90％ | 92％ | 93％ |
| Other arrangements | 84％ | 83％ | 81％ | 80％ |
Claims (2)
1. An automatic tracking device for fast tracking comprises a common automatic tracking device and a target recognition device arranged on the automatic tracking device, wherein the automatic tracking device has strong tracking capability, and the target recognition device can recognize a target according to a target contour; wherein,
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation for the arc length of the noisy profile is expressed as: gN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t) selecting a window function W (n) of width D, D ∈ {7, 9}, for curvature kN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing, and determining the curvature k 'of the noisy contour according to the comparison result'N(t)，T10.2, i.e.:
when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t)
Otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresK，
When k'N(t)|<TK*max|k′N(t) |, the characteristic function f (t) is 0
Otherwise, the feature function f (t) is 1.
2. The automatic tracking device of claim 1, further characterized by a merge module: is used for eliminating false characteristic points generated by noise interference and carrying out merging operation on characteristic points and non-characteristic points which can not form a continuous area, thereby obtaining the characteristic pointsSelecting a starting point O, extending the contour starting point to two sides, merging adjacent points, using the starting point type as the preset type of the region, and extending each S × mu to two sides0Stopping, wherein S is a preset minimum length, S is 15,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, and can effectively reduce the distortion phenomenon after combination; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × muO+1Or S × muO-1Is stopped at a time of muO+1And muO-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; adjacent areas of the same type are combined to obtain a continuous characteristic area and a non-characteristic area;
a filtering module: the multiplicative noise is related to the image signal and changes along with the change of the image signal, the wiener filtering is adopted to carry out primary filtering, and at the moment, the image information also contains residual multiplicative noiseF (x, y) q × exp (- (x) by F filter2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, in order toImprove the effect of suppressing noise, make Where σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
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Application publication date: 20160330 |