CN115393281A - Infrared weak and small target detection tracking method based on mask and adaptive filtering - Google Patents

Infrared weak and small target detection tracking method based on mask and adaptive filtering Download PDF

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CN115393281A
CN115393281A CN202210901099.7A CN202210901099A CN115393281A CN 115393281 A CN115393281 A CN 115393281A CN 202210901099 A CN202210901099 A CN 202210901099A CN 115393281 A CN115393281 A CN 115393281A
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targets
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刘洋
李晓博
邵应昭
徐常志
郑小松
张茗茗
丁跃利
文伟
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Xian Institute of Space Radio Technology
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Abstract

一种基于掩模与自适应滤波的红外弱小目标检测跟踪方法,首先利用卫星遥感侦察卫星视场固定的特点,基于初始多帧(5~10帧)红外图像序列生成一个目标前背景掩模,用于记录干扰目标与真实目标的特征信息;然后再次基础上对新输入红外图像进行检测,并根据掩模所包含的目标特征信息构建一个自适应滤波器,能够对不同目标在不同运动时刻下进行特征动态提取,进而提高目标的匹配跟踪精度。本发明方法利用目标掩模和自适应滤波器解决了红外弱小目标在高速运动时的噪声干扰和多目标干扰问题,具备更强的鲁棒性,能够实现各种复杂背景下对多个红外弱小目标的高可靠匹配跟踪,满足了敏感目标星上实时高可靠侦察需求。

Figure 202210901099

A detection and tracking method for small infrared targets based on masks and adaptive filtering. Firstly, a target foreground and background mask is generated based on the initial multi-frame (5-10 frames) infrared image sequence by using the characteristics of the fixed field of view of satellite remote sensing and reconnaissance satellites. It is used to record the characteristic information of the interference target and the real target; and then detect the new input infrared image on a basis again, and construct an adaptive filter according to the target feature information contained in the mask, which can detect different targets at different moving moments Dynamic feature extraction is performed to improve the matching and tracking accuracy of the target. The method of the present invention uses the target mask and the adaptive filter to solve the problem of noise interference and multi-target interference of infrared weak and small targets when they move at high speed, has stronger robustness, and can realize multiple infrared weak and small The high-reliability matching and tracking of targets meets the real-time and high-reliability reconnaissance requirements of sensitive targets on the planet.

Figure 202210901099

Description

一种基于掩模与自适应滤波的红外弱小目标检测跟踪方法A Method for Detection and Tracking of Small Infrared Targets Based on Mask and Adaptive Filtering

技术领域technical field

本发明设计一种红外弱小目标检测跟踪方法,特别是一种面向于高速红外弱小目标的在轨快速高可靠检测跟踪方法,属于航天遥感领域。The invention designs a method for detecting and tracking small and small infrared targets, in particular an on-orbit fast and highly reliable detection and tracking method for small and high-speed infrared targets, belonging to the field of aerospace remote sensing.

背景技术Background technique

红外相机主要通过接收目标自身的红外辐射进行观测,尤其是对导弹、飞机等高速高热辐射目标有显著的敏感性,使红外目标检测与跟踪技术在军事侦察和预警方面发挥重要作用。对于宽幅卫星红外遥感图像,这些敏感飞行目标的尺寸很小,往往呈现为斑状或点状,目标的信噪比很低,易受噪声、杂波或者云层干扰,经常会淹没于背景之中。因此,对于红外弱小目标,通常利用目标运动或变化特性进行检测,例如帧间差分法和背景差分法,具有计算简单直接、速度快的特点,使其更适用于资源有限的卫星在轨实时侦察预警任务。Infrared cameras mainly observe by receiving the infrared radiation of the target itself, especially for missiles, aircraft and other high-speed and high-heat radiation targets, which make infrared target detection and tracking technology play an important role in military reconnaissance and early warning. For wide-range satellite infrared remote sensing images, these sensitive flying targets are small in size and often appear as spots or dots. The signal-to-noise ratio of the targets is very low, and they are easily disturbed by noise, clutter or clouds, and are often submerged in the background. . Therefore, for small and weak infrared targets, the motion or change characteristics of the target are usually used for detection, such as the frame difference method and the background difference method, which have the characteristics of simple, direct and fast calculation, making it more suitable for real-time reconnaissance on-orbit by satellites with limited resources early warning mission.

但是帧间差分法和背景差分法对噪声和目标运动引起的背景变化等干扰因素较为敏感。而且,在目标发生遮挡的情况时,算法很容易将再次出现的目标误认为是另一个运动目标,不能应对遮挡变化。此外,由于帧间差分法主要是利用灰度值的差值来检测运动目标,当目标内部大部分像素点的灰度值相同时,帧间差分法得到的差分图像只包含目标物体的两侧图像,而在目标内部产生了“空洞”现象,难以得到目标的完整轮廓。而且由于目标运动状态多变,背景差分法所构造的背景难以完全剔除各种真实目标。However, the inter-frame difference method and the background difference method are more sensitive to interference factors such as background changes caused by noise and target motion. Moreover, when the target is occluded, the algorithm can easily mistake the reappearing target as another moving target and cannot cope with occlusion changes. In addition, since the inter-frame difference method mainly uses the difference in gray value to detect moving objects, when the gray values of most of the pixels inside the target are the same, the difference image obtained by the inter-frame difference method only includes the two sides of the target object. image, but a "hole" phenomenon is generated inside the target, and it is difficult to obtain a complete outline of the target. Moreover, due to the changeable motion state of the target, it is difficult to completely eliminate various real targets from the background constructed by the background difference method.

发明内容Contents of the invention

本发明解决的技术问题是:克服现有技术不足,解决了卫星在轨红外弱小目标检测跟踪虚警高、轨迹关联差(目标轨迹点不完整或者发生遮挡时跟踪丢失,再出现时无法将两条轨迹关联起来)的问题,提供了一种实时的高精度红外弱小目标检测跟踪方法。The technical problem solved by the present invention is: to overcome the deficiencies in the prior art, and to solve the problem of high false alarms and poor trajectory correlation in the detection and tracking of small and small infrared targets on orbit (the target trajectory point is incomplete or the tracking is lost when occlusion occurs, and the two objects cannot be separated when they reappear). trajectories), and provides a real-time high-precision infrared weak and small target detection and tracking method.

本发明的技术解决方案是:Technical solution of the present invention is:

一种基于掩模与自适应滤波的红外弱小目标检测跟踪方法,包括如下步骤:A method for detecting and tracking infrared dim and small targets based on masks and adaptive filtering, comprising the following steps:

1)利用上一帧图像It-1对应的背景图像

Figure BDA0003770912620000021
从当前帧的图像It中提取获得多个疑似目标,获得由当前帧中多个疑似目标坐标组成的疑似目标坐标集
Figure BDA0003770912620000022
和每个疑似目标的图像切片
Figure BDA0003770912620000023
若当前帧的疑似目标集为空时,进入步骤6);反之进入步骤2);1) Use the background image corresponding to the previous frame image I t-1
Figure BDA0003770912620000021
Extract multiple suspected targets from the image I t of the current frame, and obtain a suspected target coordinate set composed of multiple suspected target coordinates in the current frame
Figure BDA0003770912620000022
and image slices for each suspected target
Figure BDA0003770912620000023
If the suspected target set of the current frame is empty, enter step 6); otherwise, enter step 2);

2)对于步骤1)获得的疑似目标坐标集

Figure BDA0003770912620000024
中的第k个疑似目标的坐标
Figure BDA0003770912620000025
在当前帧图像It中以
Figure BDA0003770912620000026
位置处的像素点为中心提取一个图像块,对图像块进行边缘检测,将不符合目标尺寸范围的疑似目标从中疑似目标坐标集
Figure BDA0003770912620000027
剔除,遍历疑似目标坐标集
Figure BDA0003770912620000028
中的所有元素,然后进入步骤3);2) For the suspected target coordinate set obtained in step 1)
Figure BDA0003770912620000024
The coordinates of the kth suspected target in
Figure BDA0003770912620000025
In the current frame image I t with
Figure BDA0003770912620000026
The pixel at the position is taken as the center to extract an image block, and the edge detection is performed on the image block, and the suspected target that does not meet the target size range is extracted from the suspected target coordinate set
Figure BDA0003770912620000027
Eliminate, traverse the suspected target coordinate set
Figure BDA0003770912620000028
All elements in , then go to step 3);

3)根据疑似目标坐标集

Figure BDA0003770912620000029
中的第k个疑似目标的坐标
Figure BDA00037709126200000210
从上一帧图像It-1中提取出疑似目标k对应的多个候选匹配目标组成候选匹配目标集,并获得每个候选匹配目标的图片切片
Figure BDA00037709126200000211
3) According to the suspected target coordinate set
Figure BDA0003770912620000029
The coordinates of the kth suspected target in
Figure BDA00037709126200000210
Extract multiple candidate matching targets corresponding to the suspected target k from the previous frame image I t-1 to form a candidate matching target set, and obtain a picture slice of each candidate matching target
Figure BDA00037709126200000211

4)若某疑似目标k对应的候选匹配目标集

Figure BDA00037709126200000212
为空,则判定疑似目标k为新检测到的疑似目标,将疑似目标k的信息赋值到掩膜集合M{m×n}中,并返回步骤3),对下一个疑似目标进行处理,直至遍历所有疑似目标后进入步骤6);反之,则进入步骤5);掩膜集合M{m×n}由m行n列的元素组成,m等于图像It中长度方向上像素的个数,n等于图像It中宽度方向上像素的个数;每个元素内包括用于表征图像上对应像素点的信息;4) If the candidate matching target set corresponding to a suspected target k
Figure BDA00037709126200000212
If it is empty, it is determined that the suspected target k is a newly detected suspected target, and the information of the suspected target k is assigned to the mask set M{m×n}, and returns to step 3), and the next suspected target is processed until Go to step 6) after traversing all suspected targets; otherwise, go to step 5); the mask set M{m×n} is composed of elements in m rows and n columns, m is equal to the number of pixels in the length direction of the image I t , n is equal to the number of pixels in the width direction in the image I t ; each element includes the information used to characterize the corresponding pixel on the image;

5)利用步骤1)获得的疑似目标图像切片

Figure BDA00037709126200000213
和步骤3)获得的候选匹配目标图片切片
Figure BDA00037709126200000214
确定疑似目标k与对应的Jk个候选匹配目标的匹配系数,提取出满足阈值要求的候选匹配目标作为疑似目标k的候选目标
Figure BDA00037709126200000215
对掩膜集合M{m×n}中与候选目标
Figure BDA00037709126200000216
位置对应元素的信息进行更新;若某疑似目标k不存在对应的候选目标
Figure BDA0003770912620000031
则判定疑似目标k为新检测到的疑似目标,将疑似目标k的信息赋值到掩膜集合M{m×n}中,然后返回步骤3),对下一个疑似目标进行处理,直至遍历所有疑似目标后进入步骤6);5) Using the suspected target image slice obtained in step 1)
Figure BDA00037709126200000213
and the candidate matching target picture slice obtained in step 3)
Figure BDA00037709126200000214
Determine the matching coefficient between the suspected target k and the corresponding J k candidate matching targets, and extract the candidate matching targets that meet the threshold requirements as the candidate targets for the suspected target k
Figure BDA00037709126200000215
For the mask set M{m×n} and the candidate target
Figure BDA00037709126200000216
The information of the element corresponding to the position is updated; if there is no corresponding candidate target for a certain suspected target k
Figure BDA0003770912620000031
Then it is determined that the suspected target k is a newly detected suspected target, and the information of the suspected target k is assigned to the mask set M{m×n}, and then returns to step 3), and the next suspected target is processed until all suspected targets are traversed. Go to step 6 after the goal);

6)利用掩膜集合M{m×n},对上一帧图像It-1对应的背景图像

Figure BDA0003770912620000032
进行更新,获得当前帧图像It对应的背景图像
Figure BDA0003770912620000033
当前帧的疑似目标集为空时,前帧图像It对应的背景图像
Figure BDA0003770912620000034
等于上一帧图像It-1对应的背景图像
Figure BDA0003770912620000035
6) Using the mask set M{m×n}, for the background image corresponding to the previous frame image I t-1
Figure BDA0003770912620000032
Update to obtain the background image corresponding to the current frame image I t
Figure BDA0003770912620000033
When the suspected target set of the current frame is empty, the background image corresponding to the previous frame image I t
Figure BDA0003770912620000034
Equal to the background image corresponding to the previous frame image I t-1
Figure BDA0003770912620000035

7)利用掩膜集合M{m×n},判读疑似目标为真实目标还是干扰目标,并对掩膜集合M{m×n}中该疑似目标对应元素的信息进行更新;7) Use the mask set M{m×n} to judge whether the suspected target is a real target or an interference target, and update the information of the corresponding element of the suspected target in the mask set M{m×n};

8)利用掩膜集合M{m×n}中真实目标对应元素的信息,进行轨迹关联,完成对同一目标的完整跟踪。8) Use the information of the corresponding elements of the real target in the mask set M{m×n} to perform trajectory association and complete the complete tracking of the same target.

优选地,所述掩膜集合M{m×n}中每个元素内用于表征图像上对应像素点的信息采用6个特征向量进行表示,分别为:Preferably, the information used to characterize the corresponding pixel on the image in each element in the mask set M{m×n} is represented by 6 feature vectors, which are:

M{i}{1}为元素M{i}对应位置所属目标类型;目标类型包括:背景点,对应M{i}{1}值为0;干扰目标,对应M{i}{1}值为1;疑似目标,对应M{i}{1}值为2;真实目标,对应M{i}{1}值为3;1≤i≤m×n;M{i}{1} is the target type of the location corresponding to the element M{i}; the target type includes: background point, corresponding to M{i}{1} value 0; interference target, corresponding to M{i}{1} value is 1; for suspected targets, the value corresponding to M{i}{1} is 2; for real targets, the value corresponding to M{i}{1} is 3; 1≤i≤m×n;

M{i}{2}为元素M{i}对应位置所属目标的编号;M{i}{2} is the number of the target to which the position corresponding to the element M{i} belongs;

M{i}{3}为元素M{i}对应位置所属目标最新时刻的在图像长、宽方向上的像素个数;M{i}{3} is the number of pixels in the length and width directions of the image at the latest moment of the target corresponding to the position of element M{i};

M{i}{4}为元素M{i}对应位置所属目标的完整轨迹集;完整轨迹集由轨迹对应目标的中心元素在每帧图像中的位置信息组成;M{i}{4} is the complete trajectory set of the target corresponding to the position of element M{i}; the complete trajectory set is composed of the position information of the central element of the target corresponding to the trajectory in each frame of image;

M{i}{5}为元素M{i}对应位置所属目标最新时刻的速度;M{i}{5} is the speed of the target at the latest moment of the position corresponding to the element M{i};

M{i}{6}为元素M{i}对应位置所属目标最新时刻的运动方向。M{i}{6} is the latest movement direction of the target to which the position corresponding to the element M{i} belongs.

优选地,所述步骤1)中获得由当前帧中多个疑似目标坐标组成的疑似目标坐标集

Figure BDA0003770912620000036
和每个疑似目标的图像切片
Figure BDA0003770912620000037
的方法,具体为:Preferably, in the step 1), a suspected target coordinate set consisting of multiple suspected target coordinates in the current frame is obtained
Figure BDA0003770912620000036
and image slices for each suspected target
Figure BDA0003770912620000037
method, specifically:

11)求解当前帧图像与上一帧图像的像素差值,获得当前帧图像与上一帧图像的差值图像Df=It-It-1;同时求解当前帧与背景帧图像的像素差值,获得当前帧图像与背景帧图像的差值图像

Figure BDA0003770912620000041
11) Solve the pixel difference between the current frame image and the previous frame image, and obtain the difference image D f =I t -I t-1 between the current frame image and the previous frame image; simultaneously solve the pixels of the current frame and the background frame image Difference, to obtain the difference image between the current frame image and the background frame image
Figure BDA0003770912620000041

12)将差值图像Df中绝对值大于Thrf的差值点或差值图像Db中绝对值大于Thrb的差值点进行保留作为候选点,从当前帧图像上找到与候选点位置对应的像素点,并生成联通区域,然后分别记录这些联通区域的中心坐标点,根据掩膜集合中与中心坐标点位置对应元素的目标类型,从中心坐标点中剔除目标类型为干扰目标的中心坐标点,将其余中心坐标点作为疑似目标的中心坐标并加入疑似目标坐标集,得到疑似目标坐标集

Figure BDA0003770912620000042
k=1,2,3,…,K;Thrf与Thrb的取值范围为8~12;12) Reserving the difference point with an absolute value greater than Thr f in the difference image D f or the difference point with an absolute value greater than Thr b in the difference image D b as a candidate point, and finding the position of the candidate point from the current frame image Corresponding pixel points, and generate Unicom areas, and then record the central coordinate points of these Unicom areas, according to the target type of the element corresponding to the position of the central coordinate point in the mask set, remove the target type from the central coordinate point as the center of the interference target Coordinate points, take the rest of the central coordinate points as the central coordinates of the suspected target and add them to the suspected target coordinate set to obtain the suspected target coordinate set
Figure BDA0003770912620000042
k=1,2,3,...,K; Thr f and Thr b range from 8 to 12;

13)对于获得的疑似目标坐标集

Figure BDA0003770912620000043
中的第k个疑似目标的坐标
Figure BDA0003770912620000044
在当前帧图像It中以
Figure BDA0003770912620000045
位置处的像素点为中心提取一个图像块,对该图像块进行边缘检测,提取出疑似目标k的边缘形状,并计算得到疑似目标k边缘形状所包围的像素构成的图像切片;13) For the obtained suspected target coordinate set
Figure BDA0003770912620000043
The coordinates of the kth suspected target in
Figure BDA0003770912620000044
In the current frame image I t with
Figure BDA0003770912620000045
The pixel at the position is the center to extract an image block, edge detection is performed on the image block, the edge shape of the suspected target k is extracted, and an image slice composed of pixels surrounded by the edge shape of the suspected target k is calculated;

14)若步骤13)得到的疑似目标k边缘形状所包围的像素构成的图像切片尺寸不在目标先验尺寸范围内,则将疑似目标k从疑似目标坐标集

Figure BDA0003770912620000046
中剔除,反之将步骤13)获得的图像切片作为疑似目标k的图像切片
Figure BDA0003770912620000047
14) If the size of the image slice formed by the pixels surrounded by the edge shape of the suspected target k obtained in step 13) is not within the range of the prior size of the target, then the suspected target k from the suspected target coordinate set
Figure BDA0003770912620000046
Otherwise, the image slice obtained in step 13) is used as the image slice of the suspected target k
Figure BDA0003770912620000047

优选地,步骤13)中所述图像块的尺寸大于目标最大尺寸的1.25~1.7倍,小于目标最大尺寸的2倍。Preferably, the size of the image block in step 13) is larger than 1.25-1.7 times of the target maximum size and smaller than 2 times of the target maximum size.

优选地,所述步骤3)从上一帧图像It-1中提取出疑似目标k对应的多个候选匹配目标组成候选匹配目标集,并获得每个候选匹配目标的图片切片

Figure BDA0003770912620000048
的方法,具体为:Preferably, the step 3) extracts a plurality of candidate matching targets corresponding to the suspected target k from the previous frame image I t-1 to form a candidate matching target set, and obtains a picture slice of each candidate matching target
Figure BDA0003770912620000048
method, specifically:

31)在上一帧图像It-1中的同样以

Figure BDA0003770912620000049
位置处的像素点为中心,选定一个大小为q×q的图像块作为目标候选区域;31) In the previous frame of image I t-1 , the same
Figure BDA0003770912620000049
The pixel at the position is the center, and an image block with a size of q×q is selected as the target candidate area;

32)结合掩模集合M{m×n},从目标候选区域中,挑选出对应掩模集合中元素的目标类型不为背景目标的元素作为候选匹配目标,组成候选匹配目标集

Figure BDA0003770912620000051
,j=1,2,3,…,Jk,其中,Jk为疑似目标k对应的目标候选区域中的候选匹配目标的个数;32) Combining with the mask set M{m×n}, from the target candidate area, select elements corresponding to the target type of the elements in the mask set that are not background targets as candidate matching targets to form a candidate matching target set
Figure BDA0003770912620000051
,j=1,2,3,...,J k , where J k is the number of candidate matching targets in the target candidate area corresponding to the suspected target k;

33)根据掩模集合M{m×n}中特征向量M{i}{3}的信息,获得每个候选匹配目标的图像切片

Figure BDA0003770912620000052
33) According to the information of the feature vector M{i}{3} in the mask set M{m×n}, obtain the image slice of each candidate matching target
Figure BDA0003770912620000052

优选地,步骤31)所述的取值范围如下:Preferably, the value range described in step 31) is as follows:

3v·Δt/r≤q≤5v·Δt/r3v·Δt/r≤q≤5v·Δt/r

其中,r为图像分辨率,v为目标最大运动速度,Δt为相相邻帧图像之间的时间差。Among them, r is the image resolution, v is the maximum moving speed of the target, and Δt is the time difference between adjacent frame images.

优选地,步骤5)确定匹配系数的方法,具体为:Preferably, step 5) determines the method for matching coefficient, specifically:

Figure BDA0003770912620000053
Figure BDA0003770912620000053

其中,α为滤波差异系数,α的取值范围为0.01~0.2;

Figure BDA0003770912620000054
为自适应滤波器DFk对疑似目标图像切片
Figure BDA0003770912620000055
的计算结果,
Figure BDA0003770912620000056
为自适应滤波器DFk对候选匹配目标图片切片
Figure BDA0003770912620000057
的计算结果;
Figure BDA0003770912620000058
Figure BDA0003770912620000059
为候选匹配目标
Figure BDA00037709126200000510
在当前帧图像It中在行方向和列方向上的预测位置;
Figure BDA00037709126200000511
为第k个疑似目标的坐标。Among them, α is the filter difference coefficient, and the value range of α is 0.01~0.2;
Figure BDA0003770912620000054
Slice suspected object images for adaptive filter DF k
Figure BDA0003770912620000055
The calculation result of
Figure BDA0003770912620000056
For the adaptive filter DF k pair candidate matching target picture slice
Figure BDA0003770912620000057
calculation results;
Figure BDA0003770912620000058
and
Figure BDA0003770912620000059
Candidate match target
Figure BDA00037709126200000510
The predicted position in the row direction and the column direction in the current frame image I t ;
Figure BDA00037709126200000511
is the coordinate of the kth suspected target.

优选地,所述

Figure BDA00037709126200000512
Figure BDA00037709126200000513
的确定方法,具体为:Preferably, the
Figure BDA00037709126200000512
and
Figure BDA00037709126200000513
The method of determination is as follows:

Figure BDA00037709126200000514
Figure BDA00037709126200000514

其中,

Figure BDA00037709126200000515
为候选匹配目标
Figure BDA00037709126200000516
在上一帧图像It-1中的位置坐标,Δt为两相邻帧之间的时间间隔,
Figure BDA00037709126200000517
为由掩膜集合M{m×n}获得的候选匹配目标
Figure BDA00037709126200000518
的运动速度,
Figure BDA00037709126200000519
为由掩膜集合M{m×n}获得的候选匹配目标
Figure BDA00037709126200000520
与图像行方向之间的夹角。in,
Figure BDA00037709126200000515
Candidate match target
Figure BDA00037709126200000516
The position coordinates in the previous frame image I t-1 , Δt is the time interval between two adjacent frames,
Figure BDA00037709126200000517
is the candidate matching target obtained from the mask set M{m×n}
Figure BDA00037709126200000518
speed of movement,
Figure BDA00037709126200000519
is the candidate matching target obtained from the mask set M{m×n}
Figure BDA00037709126200000520
Angle with the image row direction.

优选地,所述步骤6)获得当前帧图像It对应的背景图像

Figure BDA0003770912620000061
的方法,具体为:Preferably, said step 6) obtains the background image corresponding to the current frame image I t
Figure BDA0003770912620000061
method, specifically:

Figure BDA0003770912620000062
Figure BDA0003770912620000062

其中,λ为背景更新系数,λ的取值范围为0.7~0.9;

Figure BDA0003770912620000063
表示根据掩膜集合,将当前帧图像It中对应目标类型为背景或干扰目标的像素点的像素值保持不变,其余像点的像素值至零处理后获得的像素矩阵;
Figure BDA0003770912620000064
表示在当前帧图像It中,将疑似目标与真实目标所对应的像素块作为被替换区域,将被替换区域整体替换为邻域背景的像素块,并根据掩膜集合,将当前帧图像It中对应目标类型为背景与干扰目标的像素点的像素置零处理后获得的像素矩阵;邻域背景的像素块中每个像素的值等于被替换区域外侧上下左右四个方向上等大小像素块中对应像素的均值。Among them, λ is the background update coefficient, and the value range of λ is 0.7-0.9;
Figure BDA0003770912620000063
Indicates that according to the mask set, the pixel value of the pixel point corresponding to the target type in the current frame image I t is kept unchanged, and the pixel value of the remaining image points is zero-processed to obtain the pixel matrix;
Figure BDA0003770912620000064
Indicates that in the current frame image I t , the pixel block corresponding to the suspected target and the real target is used as the replaced area, and the replaced area is replaced with the pixel block of the neighborhood background as a whole, and according to the mask set, the current frame image I The pixel matrix obtained by zeroing the pixels corresponding to the target type in t as the background and the interfering target pixels; the value of each pixel in the pixel block of the neighborhood background is equal to the pixels of the same size in the four directions of the upper, lower, left, and right outside the replaced area Mean value of corresponding pixels in the block.

优选地,所述步骤7)判读疑似目标为真实目标还是干扰目标的方法,具体为:对于掩模集合中特征向量目标类型为疑似目标的元素,当目标在整个图像序列中出现的次数超过Numd或目标轨迹停止更新超过Numo时,则对该元素的对应位置所属目标的完整轨迹集的长度进行计算,若轨迹长度超过Thrtrack;若则将该元素的目标类型判定为真实目标,否则将其判定为干扰目标;Numd的取值范围等于5~10s所对应的图像帧数;Numo的取值范围等于10~15s所对应的图像帧数;Thrtrack取值范围为3~5。Preferably, the step 7) is a method for judging whether the suspected target is a real target or an interference target, specifically: for an element whose feature vector target type is a suspected target in the mask set, when the number of times the target appears in the entire image sequence exceeds Num d or when the update of the target track exceeds Numo, calculate the length of the complete track set of the target to which the corresponding position of the element belongs, if the track length exceeds Thr track ; if so, determine the target type of the element as a real target, otherwise it will be It is judged as an interference target; the value range of Num d is equal to the number of image frames corresponding to 5-10s; the value range of Numo is equal to the number of image frames corresponding to 10-15s; the value range of Thr track is 3-5.

优选地,所述步骤8)进行轨迹关联的方法,具体为:Preferably, said step 8) carries out the method for trajectory association, specifically:

当掩模集合中目标类型为真实目标的元素对应轨迹停止更新超过Numo帧时,则判定该真实目标运动完成,并将目标编号及轨迹加入到轨迹集Track中;When the track corresponding to the element whose target type is the real target in the mask set stops updating for more than Numo frames, it is determined that the real target movement is completed, and the target number and track are added to the track set Track;

当有新轨迹加入到轨迹集中时,根据轨迹集中已知轨迹的结束轨迹点的运动速度和方向进行轨迹预测,得到已知轨迹在相应新轨迹中的预测位置,通过与新轨迹的起始位置和新旧两个轨迹对应目标的运动速度及方向进行比对,从而完成目标的完整跟踪;Numo的取值范围等于10~15s所对应的图像帧数。When a new trajectory is added to the trajectory set, the trajectory prediction is performed according to the movement speed and direction of the end trajectory point of the known trajectory in the trajectory set, and the predicted position of the known trajectory in the corresponding new trajectory is obtained. Compare the speed and direction of the target corresponding to the old and new trajectories, so as to complete the complete tracking of the target; the value range of Numo is equal to the number of image frames corresponding to 10-15s.

优选地:初始的背景图像

Figure BDA0003770912620000071
中每个像素的像素值等于初始获得的N帧图像中对应位置像素点的平均值,N的取值范围为5~10。Preferably: an initial background image
Figure BDA0003770912620000071
The pixel value of each pixel in is equal to the average value of the corresponding pixel points in the initially obtained N frames of images, and the value range of N is 5-10.

本发明与现有技术相比的优点在于:The advantage of the present invention compared with prior art is:

(1)本发明通过生成一个目标掩模来记录各种类型目标信息,从而在检测时大幅降低干扰目标的影响,提供检测精度。(1) The present invention records various types of target information by generating a target mask, thereby greatly reducing the influence of interfering targets during detection and improving detection accuracy.

(2)本发明针对敏感目标在飞行过程中呈现动态变化的特点,构建了一个自适应滤波器,可全时地提取目标的特征信息,增加了匹配正确率。(2) Aiming at the characteristics of dynamic changes of sensitive targets during flight, the present invention constructs an adaptive filter, which can extract the feature information of the target at all times, and increases the matching accuracy.

(3)本发明对所有真实目标的轨迹进行了关联,可一定程度解决目标遮挡问题,结合掩模包含的目标特征,能够生成目标的完整态势信息。(3) The present invention correlates the trajectories of all real targets, which can solve the problem of target occlusion to a certain extent, and can generate complete situation information of the target in combination with the target features included in the mask.

附图说明Description of drawings

图1为本发明方法流程图;Fig. 1 is a flow chart of the method of the present invention;

图2为本发明自适应滤波器生成方法图。Fig. 2 is a diagram of a method for generating an adaptive filter according to the present invention.

具体实施方式Detailed ways

对于卫星在轨红外侦察预警系统,情报信息的可靠性和时效性是战场支援的关键,尤其是对高速飞行目标,只有实时或准实时的检测跟踪才能发挥出天基侦察预警系统的真正作用,因此选用帧差法和背景差分法进行联合检测,实现对海量红外图像序列的快速处理。但该类方法易受噪声、成像坏点、成像亮点和移动朵云的干扰,这些干扰与周围环境存在明显差异,与真实红外弱小目标类似,往往以斑或点的形式存在于视场中,导致对真实目标进行检测时虚警较高,而且当目标被遮挡后也会发生跟丢情况。但与真实目标不同的是,这些干扰目标的形状特征和运动特性与真实目标存在一定差异,因此,基于这些先验知识,本发明利用图像序列生成一个掩模信息,通过边缘检测算子提取出各类目标的完整轮廓特征用以支撑检测,同时将其在检测跟踪过程的各类重要特征信息进行实时存储分析,并利用这些信息构建自适应滤波器,进一步确认真实目标,大幅降低虚警,在保证实时处理的同时提升情报信息可靠性。For satellite on-orbit infrared reconnaissance and early warning systems, the reliability and timeliness of intelligence information is the key to battlefield support, especially for high-speed flying targets. Only real-time or quasi-real-time detection and tracking can play a real role in space-based reconnaissance and early warning systems. Therefore, the frame difference method and the background difference method are used for joint detection to realize the rapid processing of massive infrared image sequences. However, this type of method is susceptible to interference from noise, imaging bad pixels, imaging bright spots, and moving clouds. These interferences are significantly different from the surrounding environment, similar to real infrared weak and small targets, and often exist in the form of spots or points in the field of view. As a result, false alarms are high when detecting real targets, and tracking loss will also occur when the target is occluded. However, different from real targets, the shape features and motion characteristics of these interfering targets are different from real targets. Therefore, based on these prior knowledge, the present invention uses image sequences to generate a mask information, and extracts The complete contour features of various targets are used to support the detection, and at the same time, various important feature information during the detection and tracking process are stored and analyzed in real time, and the information is used to build an adaptive filter to further confirm the real target and greatly reduce false alarms. Improve the reliability of intelligence information while ensuring real-time processing.

如图1所示,本发明方法的具体实现步骤如下:As shown in Figure 1, the specific implementation steps of the inventive method are as follows:

(1)根据所获取的遥感红外图像尺寸大小m×n,预生成初始掩模集合M{m×n},其中,m为遥感红外图像的每一行像素数,n为遥感红外图像的每一列像素数,即M{m×n}包含了相机所观测视野中的所有观测点信息,对于任一个元素M{i},1≤i≤m×n,可根据其索引i找到该元素在遥感图像观测视野中所对应的位置。掩模集合M{m×n}中每个元素M{i},分别对应有6个特征向量M{i}{j},1≤i≤m×n,1≤j≤6。(1) Pre-generate an initial mask set M{m×n} according to the acquired remote sensing infrared image size m×n, where m is the number of pixels in each row of the remote sensing infrared image, and n is each column of the remote sensing infrared image The number of pixels, that is, M{m×n} contains all observation point information in the field of view observed by the camera. For any element M{i}, 1≤i≤m×n, the element can be found according to its index i The corresponding position in the image observation field of view. Each element M{i} in the mask set M{m×n} corresponds to 6 feature vectors M{i}{j}, 1≤i≤m×n, 1≤j≤6.

其中,in,

M{i}{1}为该元素对应位置所属目标类型;目标类型包括:背景点,对应M{i}{1}值为0;干扰目标,对应M{i}{1}值为1;疑似目标,对应M{i}{1}值为2;真实目标,对应M{i}{1}值为3;只有目标切片的中心点元素有值,其余为空。疑似目标包括:干扰目标和真实目标。对于背景点,M{i}包含1个特征向量,其余特征向量为空。对于疑似目标、干扰目标和真实目标,M{i}包含6个特征向量。M{i}{1} is the target type of the corresponding position of the element; the target type includes: background point, the corresponding M{i}{1} value is 0; interference target, the corresponding M{i}{1} value is 1; For suspected targets, the corresponding M{i}{1} value is 2; for real targets, the corresponding M{i}{1} value is 3; only the center point element of the target slice has a value, and the rest are empty. Suspected targets include: interference targets and real targets. For background points, M{i} contains 1 feature vector, and the rest of the feature vectors are empty. For suspected targets, jamming targets and real targets, M{i} contains 6 feature vectors.

M{i}{2}为该元素对应位置所属目标的编号;M{i}{2} is the number of the target to which the corresponding position of the element belongs;

M{i}{3}为该元素对应位置所属目标最新时刻的尺寸特征(即在遥感红外图像长、宽方向上的像素个数);M{i}{3} is the size feature of the target at the latest moment corresponding to the element's position (that is, the number of pixels in the length and width directions of the remote sensing infrared image);

M{i}{4}为该元素对应位置所属目标的完整轨迹集;完整轨迹集由轨迹对应目标的中心元素在每帧图像中的位置信息组成;M{i}{4} is the complete trajectory set of the target corresponding to the position of the element; the complete trajectory set is composed of the position information of the central element of the target corresponding to the trajectory in each frame of image;

M{i}{5}为该元素对应位置所属目标最新时刻的速度;M{i}{5} is the speed of the latest moment of the target to which the position corresponding to the element belongs;

M{i}{6}为该元素对应位置所属目标最新时刻的运动方向。M{i}{6} is the movement direction of the target at the latest moment to which the corresponding position of the element belongs.

初始掩模集合中每个元素的特征向量的初始值设置为0,即假设所有像素点初始目标类型均为背景点。The initial value of the feature vector of each element in the initial mask set is set to 0, that is, it is assumed that the initial target type of all pixels is the background point.

(2)根据初始获得的N帧遥感红外图像(N一般为5~10张图像){I1,I2,...,IN},对每个对应位置的像素点进行求和取平均处理,获得初始的背景图像

Figure BDA0003770912620000081
初始的背景图像中每个像素的像素值等于N帧遥感红外图像中对应位置像素点的平均值。(2) According to the initially obtained N frames of remote sensing infrared images (N is generally 5 to 10 images) {I 1 ,I 2 ,...,I N }, sum and average the pixels at each corresponding position processing, get the initial background image
Figure BDA0003770912620000081
The pixel value of each pixel in the initial background image is equal to the average value of the corresponding pixel points in the N frames of remote sensing infrared images.

(3)通过初始的背景图像,对当前帧的遥感红外图像进行疑似目标提取,获得由当前帧中疑似目标坐标组成的疑似目标坐标集和每个疑似目标的图像切片

Figure BDA0003770912620000091
若当前帧的疑似目标集为空时,重复步骤(3)对下一帧的遥感红外图像进行疑似目标提取,直至疑似目标坐标集不为空时,进入步骤(4);若当前帧的疑似目标集为空时,则当前帧的背景图像
Figure BDA0003770912620000092
与上一帧的背景图像相同;(3) Through the initial background image, extract the suspected target from the remote sensing infrared image of the current frame, and obtain the suspected target coordinate set composed of the suspected target coordinates in the current frame and the image slice of each suspected target
Figure BDA0003770912620000091
If the suspected target set of the current frame is empty, repeat step (3) to extract the suspected target from the remote sensing infrared image of the next frame until the suspected target coordinate set is not empty, enter step (4); When the target set is empty, the background image of the current frame
Figure BDA0003770912620000092
The same background image as the previous frame;

(31)利用当前帧图像和上一帧图像的差异性,以及当前帧图像和上一帧获得的

Figure BDA0003770912620000093
背景帧图像的差异性来对目标进行检测,分别求解当前帧图像与上一帧图像的像素差值,获得当前帧图像与上一帧图像的差值图像Df=It-It-1;同时求解当前帧与背景帧图像的像素差值,获得当前帧图像与背景帧图像的差值图像
Figure BDA0003770912620000094
差值图像中的每个点作为差值点,当前帧图像与上一帧图像的差值图像中每个差值点的像素值等于当前帧图像对应像素点的值与上一帧图像对应像素点的值之间的差值。当前帧图像与背景帧图像的差值图像中每个差值点的像素值等于当前帧图像对应像素点的值与背景帧图像对应像素点的值之间的差值。(31) Using the difference between the current frame image and the previous frame image, and the current frame image and the previous frame obtained
Figure BDA0003770912620000093
The difference of the background frame image is used to detect the target, and the pixel difference between the current frame image and the previous frame image is solved respectively to obtain the difference image D f =I t -I t-1 of the current frame image and the previous frame image ; Simultaneously solve the pixel difference between the current frame image and the background frame image, and obtain the difference image between the current frame image and the background frame image
Figure BDA0003770912620000094
Each point in the difference image is used as a difference point, and the pixel value of each difference point in the difference image between the current frame image and the previous frame image is equal to the value of the corresponding pixel point of the current frame image and the corresponding pixel of the previous frame image The difference between the values of the points. The pixel value of each difference point in the difference image between the current frame image and the background frame image is equal to the difference between the value of the corresponding pixel point of the current frame image and the value of the corresponding pixel point of the background frame image.

(32)将差值图像Df中绝对值大于Thrf的差值点或差值图像Db中绝对值大于Thrb的差值点进行保留(经统计,Thrf与Thrb一般取值范围为8~12)作为候选点,从当前帧图像上找到与候选点位置对应的像素点,并利用联通域标记方法(已有成熟方法)将这些像素点进行联通生成K个联通区域,然后分别记录这些联通区域的中心坐标点,根据掩膜集合中与中心坐标点位置对应元素的目标类型,从中心坐标点中剔除目标类型为干扰目标的中心坐标点,将其余中心坐标点作为疑似目标的中心坐标并加入疑似目标坐标集,得到疑似目标坐标集

Figure BDA0003770912620000095
k=1,2,3,…,K,其中,
Figure BDA0003770912620000096
表示第k个疑似目标中心在观测视野中处于第
Figure BDA0003770912620000097
列,第
Figure BDA0003770912620000098
行的像素点(位置)。(初始获得疑似目标坐标集时,疑似目标坐标集中每个元素的所属目标类型均为疑似目标,即
Figure BDA0003770912620000101
(32) Keep the difference points whose absolute value is greater than Thr f in the difference image D f or the difference points whose absolute value is greater than Thr b in the difference image D b (according to statistics, the general value range of Thr f and Thr b 8 to 12) as candidate points, find the pixel points corresponding to the candidate point positions from the current frame image, and use the Unicom domain marking method (the existing mature method) to connect these pixel points to generate K Unicom regions, and then respectively Record the central coordinate points of these connected areas, according to the target type of the element corresponding to the position of the central coordinate point in the mask set, remove the central coordinate points whose target type is the interference target from the central coordinate points, and use the rest of the central coordinate points as the suspected target Center coordinates and add the suspected target coordinate set to get the suspected target coordinate set
Figure BDA0003770912620000095
k=1,2,3,...,K, where,
Figure BDA0003770912620000096
Indicates that the center of the kth suspected target is in the observation field of view
Figure BDA0003770912620000097
column, No.
Figure BDA0003770912620000098
The pixel point (position) of the row. (When the suspected target coordinate set is initially obtained, the target type of each element in the suspected target coordinate set is a suspected target, that is
Figure BDA0003770912620000101

(33)对于步骤(32)获得的疑似目标坐标集中的第k个疑似目标的坐标

Figure BDA0003770912620000102
在当前帧图像It中以
Figure BDA0003770912620000103
位置处的像素点为中心提取一个图像块,图像块的尺寸大于目标最大尺寸的1.25~1.7倍,小于目标最大尺寸的2倍。(选取原因是由于红外遥感观测卫星轨道固定,其分辨率也是可确定的,因此可以利用这一先验知识统计得到目标在观测图像中的尺寸范围),并利用“sobel”索贝尔算子来对该图像块进行边缘检测,提取出疑似目标k的边缘形状,并可计算得到疑似目标大小及边缘形状所包围的像素构成疑似目标的图像切片
Figure BDA0003770912620000104
如果疑似目标k的图像切片的尺寸不在统计得到的目标对应观测图像中尺寸范围内,则将目标k从疑似目标坐标集
Figure BDA0003770912620000105
中剔除,遍历疑似目标坐标集中的所有元素,然后进入步骤(4);(33) For the coordinates of the kth suspected target in the suspected target coordinate set obtained in step (32)
Figure BDA0003770912620000102
In the current frame image I t with
Figure BDA0003770912620000103
The pixel at the position is taken as the center to extract an image block, and the size of the image block is 1.25-1.7 times larger than the maximum size of the target, and smaller than 2 times the maximum size of the target. (The reason for the selection is that the orbit of the infrared remote sensing observation satellite is fixed, and its resolution can also be determined, so this prior knowledge can be used to statistically obtain the size range of the target in the observation image), and the "sobel" Sobel operator is used to Perform edge detection on the image block, extract the edge shape of the suspected target k, and calculate the suspected target size and the pixels surrounded by the edge shape to form the image slice of the suspected target
Figure BDA0003770912620000104
If the size of the image slice of the suspected target k is not within the size range of the corresponding observed image of the target obtained by statistics, then the target k is removed from the suspected target coordinate set
Figure BDA0003770912620000105
Eliminate, traverse all elements in the suspected target coordinate set, and then enter step (4);

(4)根据步骤(3)获得的疑似目标坐标集,从上一帧图像中提取出每个疑似目标对应的候选匹配目标集和候选匹配目标图片切片

Figure BDA0003770912620000106
(4) According to the suspected target coordinate set obtained in step (3), extract the candidate matching target set and the candidate matching target picture slice corresponding to each suspected target from the previous frame image
Figure BDA0003770912620000106

(41)对于疑似目标k,在上一帧图像It-1中的同样以

Figure BDA0003770912620000107
位置处的像素点为中心,选定一个大小为q×q的图像块(图像块大小q主要结合图像分辨率r和目标最大运动速度v进行判断,3v·Δt/r≤q≤5v·Δt/r,其中Δt为相邻帧图像之间的时间差)作为目标候选区域;(41) For the suspected target k, in the previous frame of image I t-1 , the same
Figure BDA0003770912620000107
The pixel point at the position is the center, and an image block with a size of q×q is selected (the image block size q is mainly judged by combining the image resolution r and the maximum moving speed v of the target, 3v·Δt/r≤q≤5v·Δt /r, where Δt is the time difference between adjacent frame images) as the target candidate area;

(42)结合掩模集合M{m×n},从目标候选区域中,挑选出对应掩模集合中元素的目标类型不为背景目标的点,作为候选匹配目标,组成候选匹配目标集

Figure BDA0003770912620000108
,j=1,2,3,…,Jk,其中,Jk为疑似目标k对应的目标候选区域中的候选匹配目标数。根据掩模集合M{m×n}中特征向量M{i}{3}的信息,获得每个候选匹配目标的图像切片
Figure BDA0003770912620000109
(42) Combined with the mask set M{m×n}, from the target candidate area, select the points corresponding to the target type of the elements in the mask set that are not background targets, as candidate matching targets, and form a candidate matching target set
Figure BDA0003770912620000108
,j=1,2,3,...,J k , where J k is the number of candidate matching targets in the target candidate area corresponding to the suspected target k. According to the information of the feature vector M{i}{3} in the mask set M{m×n}, the image slice of each candidate matching target is obtained
Figure BDA0003770912620000109

(43)重复步骤(41)~(42)K次,获得每个疑似目标的候选匹配目标集。(43) Repeat steps (41)-(42) K times to obtain a candidate matching target set for each suspected target.

(5)若某疑似目标k对应的候选匹配目标集

Figure BDA0003770912620000111
为空,则说明当前帧中的疑似目标k为新检测到的疑似目标(对应
Figure BDA0003770912620000112
值为2),将新编号赋值到掩膜集合
Figure BDA0003770912620000113
中,并将尺寸特征和目标位置分别赋值到
Figure BDA0003770912620000114
Figure BDA0003770912620000115
完成该帧目标检测,进入步骤(7);(5) If the candidate matching target set corresponding to a suspected target k
Figure BDA0003770912620000111
is empty, it means that the suspected target k in the current frame is a newly detected suspected target (corresponding to
Figure BDA0003770912620000112
The value is 2), assign the new number to the mask set
Figure BDA0003770912620000113
, and assign the size feature and target position to
Figure BDA0003770912620000114
and
Figure BDA0003770912620000115
Complete the frame target detection, enter step (7);

若候选匹配目标集{TP_k(j)}不为空,进入步骤(6);If the candidate matching target set {T P _k(j)} is not empty, go to step (6);

(6)利用步骤(3)获得的疑似目标图像切片

Figure BDA0003770912620000116
和步骤(4)获得的候选匹配目标图片切片
Figure BDA0003770912620000117
分别确定每个疑似目标与对应的Jk个候选匹配目标的匹配系数,提取出满足阈值要求的候选匹配目标作为疑似目标k的候选目标
Figure BDA0003770912620000118
根据候选目标
Figure BDA0003770912620000119
获得疑似目标k的运动方向及速度,并对掩模中候选目标
Figure BDA00037709126200001110
对应的元素的特征向量进行更新;若某疑似目标k不存在对应的候选目标
Figure BDA00037709126200001111
则定义疑似目标k为新检测到的疑似目标(即
Figure BDA00037709126200001112
),更新掩模中对应位置的元素的特征向量;(6) Using the suspected target image slice obtained in step (3)
Figure BDA0003770912620000116
and the candidate matching target picture slice obtained in step (4)
Figure BDA0003770912620000117
Determine the matching coefficients of each suspected target and the corresponding J k candidate matching targets, and extract the candidate matching targets that meet the threshold requirements as the candidate targets of suspected target k
Figure BDA0003770912620000118
According to the candidate target
Figure BDA0003770912620000119
Obtain the movement direction and speed of the suspected target k, and compare the candidate targets in the mask
Figure BDA00037709126200001110
The feature vector of the corresponding element is updated; if there is no corresponding candidate target for a suspected target k
Figure BDA00037709126200001111
Then define the suspected target k as the newly detected suspected target (ie
Figure BDA00037709126200001112
), update the feature vector of the element at the corresponding position in the mask;

(61)利用步骤(3)获得的疑似目标图像切片

Figure BDA00037709126200001113
和步骤(4)获得的候选匹配目标图片切片
Figure BDA00037709126200001114
利用一个与目标大小相同的自适应滤波器DFk对疑似目标k与疑似目标k对应的候选匹配目标集
Figure BDA00037709126200001115
中所有候选匹配目标进行匹配,匹配系数的计算如下所示:(61) Using the suspected target image slice obtained in step (3)
Figure BDA00037709126200001113
and the candidate matching target picture slice obtained in step (4)
Figure BDA00037709126200001114
Use an adaptive filter DF k with the same size as the target to pair the candidate matching target set corresponding to the suspected target k and the suspected target k
Figure BDA00037709126200001115
Match all candidate matching targets in , and the calculation of the matching coefficient is as follows:

Figure BDA00037709126200001116
Figure BDA00037709126200001116

上式中,α为滤波差异系数(α一般取0.01~0.2,与图像像素值动态范围为负相关),

Figure BDA00037709126200001117
为自适应滤波器DFk对疑似目标图像切片
Figure BDA00037709126200001118
的计算结果,
Figure BDA00037709126200001119
为自适应滤波器DFk对候选匹配目标图片切片
Figure BDA00037709126200001120
的计算结果;
Figure BDA00037709126200001121
为疑似目标k在图像It中所占据的所有像素构成的矩阵,即疑似目标图像切片(切片主要利用步骤33中的边缘检测结果进行提取),
Figure BDA0003770912620000121
为第j个候选匹配目标
Figure BDA0003770912620000122
在图像It-1中的候选匹配目标图像切片(表示目标所占据的所有像素构成的矩阵,并等比例缩放到与图像It相同大小),
Figure BDA0003770912620000123
Figure BDA0003770912620000124
为候选匹配目标
Figure BDA0003770912620000125
在当前帧图像It中在行方向和列方向上的预测位置,计算方法如下:In the above formula, α is the filter difference coefficient (α is generally 0.01 to 0.2, which is negatively correlated with the dynamic range of image pixel values),
Figure BDA00037709126200001117
Slice suspected object images for adaptive filter DF k
Figure BDA00037709126200001118
The calculation result of
Figure BDA00037709126200001119
For the adaptive filter DF k pair candidate matching target picture slice
Figure BDA00037709126200001120
calculation results;
Figure BDA00037709126200001121
Be the matrix formed by all pixels occupied by the suspected target k in the image I t , i.e. the suspected target image slice (the slice mainly utilizes the edge detection result in step 33 to extract),
Figure BDA0003770912620000121
matches the target for the jth candidate
Figure BDA0003770912620000122
Candidate matching target image slice in image I t-1 (representing a matrix composed of all pixels occupied by the target, and scaled to the same size as image I t ),
Figure BDA0003770912620000123
and
Figure BDA0003770912620000124
Candidate match target
Figure BDA0003770912620000125
The prediction position in the row direction and the column direction in the current frame image I t is calculated as follows:

Figure BDA0003770912620000126
Figure BDA0003770912620000126

上式中,

Figure BDA0003770912620000127
为候选匹配目标
Figure BDA0003770912620000128
在图像It-1中的位置,Δt为两相邻帧之间的时间间隔,
Figure BDA0003770912620000129
Figure BDA00037709126200001210
分别为候选匹配目标
Figure BDA00037709126200001211
的运动速度和运动方向(方向为运动方向与行方向x之间的夹角)。In the above formula,
Figure BDA0003770912620000127
Candidate match target
Figure BDA0003770912620000128
At the position in the image I t-1 , Δt is the time interval between two adjacent frames,
Figure BDA0003770912620000129
and
Figure BDA00037709126200001210
Candidate matching targets
Figure BDA00037709126200001211
The movement speed and direction of movement (the direction is the angle between the movement direction and the row direction x).

在构建动态滤波器时,由于目标在红外遥感图像中的成像大小一般在1×1到10×10范围内进行连续变化,而高速运动目标由于其尾焰温度较高,红外相机在成像时也会将部分尾焰进行记录,导致目标在高速运动时在图像中形成一个具有方向的椭圆形亮斑,亮斑的轴线方向与目标运动方向相近。对于动态变化的红外弱小目标,如果使用一个固定的滤波器对其进行特征提取,难以实现对目标的时刻稳定捕捉。因此,本发明根据目标特征设计了一个矩形的带有角度的自适应滤波器,该滤波器以一个5×5的滤波器DFbase为基准,根据目标k的长lk和宽wk生成自适应滤波器DFk,DFk的长为lk+2,宽为wk+2,其滤波器系数由DFbase系数进行插值或抽样产生,其方向与目标k的轴线方向相同,如图2所示。When constructing a dynamic filter, since the imaging size of the target in the infrared remote sensing image generally changes continuously in the range of 1×1 to 10×10, and the high-speed moving target has a high temperature of the tail flame, the infrared camera also needs to perform imaging. Part of the tail flame will be recorded, resulting in the formation of an elliptical bright spot with direction in the image when the target is moving at high speed, and the axis direction of the bright spot is similar to the direction of target movement. For the dynamically changing weak infrared targets, if a fixed filter is used for feature extraction, it is difficult to achieve stable capture of the target at all times. Therefore , the present invention designs a rectangular adaptive filter with an angle according to the target feature. The filter is based on a 5×5 filter DF base , and is generated from Adaptive filter DF k , the length of DF k is l k +2, the width is w k +2, its filter coefficients are generated by interpolation or sampling of DF base coefficients, and its direction is the same as the axis direction of target k, as shown in Figure 2 shown.

(62)可以看出,疑似目标k与候选匹配目标

Figure BDA00037709126200001212
之间差异越大,表示匹配系数
Figure BDA0003770912620000131
越大;
Figure BDA0003770912620000132
越小,表示疑似目标k与候选匹配目标
Figure BDA0003770912620000133
越相似,在所有满足
Figure BDA0003770912620000134
(ThrR为一个统计量,根据任务不同,设计也不同,在本例中为2~5)的候选匹配目标集中,选择匹配系数值最小的候选匹配目标为k所匹配的候选目标
Figure BDA0003770912620000135
即认为候选目标
Figure BDA0003770912620000136
与疑似目标k为同一目标,可通过当前帧与上一帧位置的变化计算出目标k的运动方向及速度,在此基础上将目标的类型、编号保持不变、目标尺寸、轨迹集、速度、运动方向特征于掩模
Figure BDA0003770912620000137
处更新,并消除掩模
Figure BDA0003770912620000138
处的历史特征信息;若不存在匹配系数小于ThrR的目标,同样认为该目标为新出现目标,则其建立新编号,并在掩模
Figure BDA0003770912620000139
处将目标的编号、尺寸、轨迹进行更新。(62) It can be seen that the suspected target k and the candidate matching target
Figure BDA00037709126200001212
The greater the difference, the matching coefficient
Figure BDA0003770912620000131
bigger;
Figure BDA0003770912620000132
The smaller the value, it means that the suspected target k matches the candidate target
Figure BDA0003770912620000133
The more similar, in all satisfies
Figure BDA0003770912620000134
(Thr R is a statistic, depending on the task, the design is also different, in this example, it is 2 to 5) in the candidate matching target set, select the candidate matching target with the smallest matching coefficient value as the candidate target matched by k
Figure BDA0003770912620000135
candidate target
Figure BDA0003770912620000136
It is the same target as the suspected target k, and the movement direction and speed of the target k can be calculated through the position change between the current frame and the previous frame. On this basis, the type, number, target size, trajectory set, and speed of the target remain unchanged. , the direction of motion is characterized by the mask
Figure BDA0003770912620000137
update and remove the mask
Figure BDA0003770912620000138
The historical feature information at the place; if there is no target with a matching coefficient less than Thr R , it is also considered as a new target, then it will create a new number, and in the mask
Figure BDA0003770912620000139
Update the number, size and trajectory of the target.

(7)在完成当前帧图像It检测后,利用掩模M完成对当前帧对应的背景图像

Figure BDA00037709126200001310
中疑似目标及真实目标对应的像素值更新,更新原则为:(7) After completing the current frame image I t detection, use the mask M to complete the background image corresponding to the current frame
Figure BDA00037709126200001310
The pixel values corresponding to the suspected target and the real target are updated, and the update principle is:

Figure BDA00037709126200001311
Figure BDA00037709126200001311

上式中,λ为背景更新系数,一般为(0.7~0.9)之间,

Figure BDA00037709126200001312
表示根据掩膜集合,从当前帧图像It中提取出目标类型为背景或干扰目标的像素点的像素值保持不变,其余像点(疑似目标与真实目标像素点)的像素值至零处理后获得的像素矩阵(背景、干扰目标、疑似目标以及真实目标所对应的像素点可以根据M{i}{1}与M{i}{3}的值得到)。
Figure BDA00037709126200001313
表示在当前帧图像It基础上,将疑似目标与真实目标所在对应的像素块整体替换为该目标所在邻域背景(邻域背景表示目标在图像It中所在位置上下左右四个方向的背景像素)像素块,邻域背景像素块的大小取决于对应目标的大小,由目标切片周围上下左右四个相同大小的背景像素块平均加权得到,并将背景与干扰目标所对应的像素置零。In the above formula, λ is the background update coefficient, generally between (0.7 and 0.9),
Figure BDA00037709126200001312
Indicates that according to the mask set, the pixel values of the pixels whose target type is background or interference target extracted from the current frame image I t remain unchanged, and the pixel values of the remaining pixels (suspected targets and real target pixels) are processed to zero The obtained pixel matrix (the pixels corresponding to the background, interference target, suspected target and real target can be obtained according to the values of M{i}{1} and M{i}{3}).
Figure BDA00037709126200001313
Indicates that on the basis of the current frame image I t , the pixel blocks corresponding to the suspected target and the real target are replaced as a whole with the background of the neighborhood where the target is located (neighborhood background indicates the background of the target in the image I t in the four directions of up, down, left, and right pixel) pixel block, the size of the neighborhood background pixel block depends on the size of the corresponding target, which is obtained by the average weighting of four background pixel blocks of the same size around the target slice, and the pixels corresponding to the background and the interference target are set to zero.

(8)对于掩模集合中特征向量目标类型为疑似目标的元素,当目标在整个图像序列中出现的次数超过Numd(一般为成像5~10s所对应的图像帧数)或目标轨迹停止更新超过Numo(一般为成像10~15s所对应的图像帧数,即目标如果连续在10~15s图像序列中没有新的轨迹,判定该目标已消失)帧时,对其轨迹长度进行计算,若轨迹长度超过Thrtrack(Thrtrack取值范围为3~5,本发明实施例中为3,即目标移动超过3个像素),则将该元素的目标类型判定为真实目标,否则将其判定为干扰目标,并对掩模M进行更新,更新原则为:若为真实目标,于

Figure BDA0003770912620000141
处更新目标类型、编号、外形特征、目标速度、运动方向;若为干扰目标,则只保留类型信息,特征向量M{i}{2}~M{i}{6}的值均为空,即:(8) For elements in the mask set whose feature vector target type is a suspected target, when the number of times the target appears in the entire image sequence exceeds Num d (generally the number of image frames corresponding to imaging 5-10s) or the target trajectory stops updating When it exceeds Numo (generally the number of image frames corresponding to 10-15s of imaging, that is, if the target has no new trajectory in the image sequence of 10-15s continuously, it is judged that the target has disappeared), calculate its trajectory length, if the trajectory If the length exceeds Thr track (Thr track ranges from 3 to 5, in the embodiment of the present invention it is 3, that is, the target moves more than 3 pixels), then the target type of the element is determined as a real target, otherwise it is determined as interference target, and update the mask M, the update principle is: if it is a real target, then
Figure BDA0003770912620000141
Update the target type, serial number, appearance feature, target speed, and direction of motion; if it is an interference target, only the type information will be kept, and the values of the feature vectors M{i}{2}~M{i}{6} are all empty, which is:

Figure BDA0003770912620000142
Figure BDA0003770912620000142

(9)对于掩模集合中特征向量目标类型为真实目标的元素OT,当目标轨迹停止更新超过Numo帧时,判定该目标运动完成,并将目标编号及轨迹加入到轨迹集Track中。对于新轨迹

Figure BDA0003770912620000143
为第l条轨迹在第t帧图像中的坐标位置,tb(l)为第l条轨迹的起始出现帧,te(l)为第l条轨迹的末尾出现帧。当新轨迹Track(l+1)加入到轨迹集中时,根据轨迹中结束轨迹点的运动速度和方向进行轨迹预测,得到其在相应新轨迹中的预测位置,通过与新轨迹的起始位置和新旧两个轨迹对应目标的运动速度及方向进行比对,从而完成目标的完整跟踪。(9) For the element O T in the mask set whose eigenvector target type is a real target, when the target track stops updating for more than Numo frames, it is judged that the target movement is completed, and the target number and track are added to the track set Track. for new track
Figure BDA0003770912620000143
is the coordinate position of the lth track in the image frame t, t b (l) is the initial frame of the lth track, and t e (l) is the end frame of the lth track. When the new trajectory Track(l+1) is added to the trajectory set, the trajectory prediction is performed according to the motion speed and direction of the end trajectory point in the trajectory, and its predicted position in the corresponding new trajectory is obtained. The old and new trajectories are compared against the moving speed and direction of the target, so as to complete the complete tracking of the target.

本发明虽然已以较佳实施例公开如上,但其并不是用来限定本发明,任何本领域技术人员在不脱离本发明的精神和范围内,都可以利用上述揭示的方法和技术内容对本发明技术方案做出可能的变动和修改,因此,凡是未脱离本发明技术方案的内容,依据本发明的技术实质对以上实施例所作的任何简单修改、等同变化及修饰,均属于本发明技术方案的保护范围。在不冲突的情况下,本申请实施例以及实施例中的技术特征可以相互组合。Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention, and any person skilled in the art can use the methods disclosed above and technical content to analyze the present invention without departing from the spirit and scope of the present invention. Possible changes and modifications are made in the technical solution. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention, which do not depart from the content of the technical solution of the present invention, all belong to the technical solution of the present invention. protected range. In the case of no conflict, the embodiments of the present application and the technical features in the embodiments may be combined with each other.

本发明说明书中未作详细描述的内容属本领域技术人员的公知技术。The content that is not described in detail in the description of the present invention belongs to the well-known technology of those skilled in the art.

Claims (12)

1. A method for detecting and tracking infrared dim targets based on masks and adaptive filtering is characterized by comprising the following steps:
1) Using the last frame image I t-1 Corresponding background image
Figure FDA0003770912610000011
From picture I of the current frame t Extracting to obtain multiple suspected targets, and obtaining a suspected target coordinate set composed of multiple suspected target coordinates in the current frame
Figure FDA0003770912610000012
And image slices of each suspected object
Figure FDA0003770912610000013
If the suspected target set of the current frame is empty, entering step 6); otherwise, entering the step 2);
2) For the suspected target coordinate set obtained in step 1)
Figure FDA0003770912610000014
Coordinates of the kth suspected target in (1)
Figure FDA0003770912610000015
In the current frame image I t To Chinese
Figure FDA0003770912610000016
Extracting an image block by taking a pixel point at the position as the center, carrying out edge detection on the image block, and carrying out suspected target coordinate set on a suspected target which does not conform to the size range of the target
Figure FDA0003770912610000017
Removing and traversing suspected target coordinate set
Figure FDA0003770912610000018
Then go to step 3);
3) According to the suspected target coordinate set
Figure FDA0003770912610000019
Coordinates of the kth suspected target in (1)
Figure FDA00037709126100000110
From the last frame image I t-1 Extracting a plurality of candidate matching targets corresponding to the suspected target k to form a candidate matching target set, and obtaining a picture slice of each candidate matching target
Figure FDA00037709126100000111
4) If a candidate matching target set corresponding to a suspected target k
Figure FDA00037709126100000112
If the suspected target k is empty, judging the suspected target k as a newly detected suspected target, assigning the information of the suspected target k to a mask set M { M × n }, returning to the step 3), processing the next suspected target until all the suspected targets are traversed, and entering the step 6); otherwise, entering the step 5); the set of masks M { M × n } is composed of M rows and n columns of elements, M being equal to the image I t Number of pixels in the medium-length direction, n being equal to image I t The number of pixels in the medium width direction; each element comprises information used for representing a corresponding pixel point on the image;
5) By using stepsStep 1) obtaining a suspected target image slice
Figure FDA00037709126100000113
And 3) obtaining candidate matching target picture slices
Figure FDA00037709126100000114
Determining a suspected target k and a corresponding J k Extracting the candidate matching target meeting the threshold requirement as the candidate target of the suspected target k according to the matching coefficient of each candidate matching target
Figure FDA0003770912610000021
For the neutralization candidate target in the mask set M { M × n }
Figure FDA0003770912610000022
Updating the information of the elements corresponding to the positions; if some suspected target k does not have a corresponding candidate target
Figure FDA0003770912610000023
Judging the suspected target k as a newly detected suspected target, assigning the information of the suspected target k to a mask set M { M × n }, then returning to the step 3), processing the next suspected target until all the suspected targets are traversed, and then entering the step 6);
6) Using mask set M { M × n }, for the previous frame image I t-1 Corresponding background image
Figure FDA0003770912610000024
Updating to obtain current frame image I t Corresponding background image
Figure FDA0003770912610000025
When the suspected target set of the current frame is empty, the image I of the previous frame t Corresponding background image
Figure FDA0003770912610000026
Is equal toOne frame image I t-1 Corresponding background image
Figure FDA0003770912610000027
7) Judging whether the suspected target is a real target or an interference target by using the mask set M { M multiplied by n }, and updating information of elements corresponding to the suspected target in the mask set M { M multiplied by n };
8) And (4) carrying out track association by using the information of the corresponding elements of the real target in the mask set M { M multiplied by n } to complete the complete tracking of the same target.
2. The method for detecting and tracking the infrared weak and small target based on the mask and the adaptive filtering as claimed in claim 1, wherein the information used for characterizing the corresponding pixel points on the image in each element in the mask set M { M × n } is represented by 6 eigenvectors, which are respectively:
m { i } {1} is the target type of the corresponding position of the element M { i }; the object types include: a background point corresponding to a value of M { i } {1} of 0; an interference target, corresponding to a value of 1 for M { i } {1 }; a suspected target, corresponding to a value of M { i } {1} of 2; the real target, corresponding to M { i } {1} value is 3; i is more than or equal to 1 and less than or equal to mxn;
m { i } {2} is the number of the target to which the corresponding position of the element M { i } belongs;
m { i } {3} is the number of pixels in the length and width directions of the image at the latest moment of the target to which the corresponding position of the element M { i } belongs;
m { i } {4} is a complete track set of the target to which the corresponding position of the element M { i } belongs; the complete track set consists of position information of central elements of the track corresponding to the target in each frame of image;
m { i } {5} is the speed of the target latest moment to which the corresponding position of the element M { i } belongs;
m { i } {6} is the motion direction of the object at the latest moment to which the corresponding position of the element M { i } belongs.
3. The method for detecting and tracking infrared dim target based on mask and adaptive filtering as claimed in claim 2, wherein said step 1) is obtained from the current frameA suspected target coordinate set composed of multiple suspected target coordinates
Figure FDA0003770912610000031
And image slices of each suspected object
Figure FDA0003770912610000032
The method specifically comprises the following steps:
11 Solving the pixel difference between the current frame image and the previous frame image to obtain a difference image D between the current frame image and the previous frame image f =I t -I t-1 (ii) a Simultaneously solving the pixel difference value of the current frame image and the background frame image to obtain a difference value image of the current frame image and the background frame image
Figure FDA0003770912610000033
12 Differential image D) f Medium absolute value is greater than Thr f Difference point or difference image D of b Medium absolute value is greater than Thr b The difference points are reserved as candidate points, pixel points corresponding to the candidate points are found from the current frame image, communication areas are generated, then central coordinate points of the communication areas are recorded respectively, according to the target type of elements corresponding to the central coordinate points in the mask set, the central coordinate points with the target type as an interference target are removed from the central coordinate points, the rest central coordinate points are used as the central coordinates of the suspected target and are added into the suspected target coordinate set, and the suspected target coordinate set is obtained
Figure FDA0003770912610000034
Thr f And Thr b The value range of (A) is 8-12;
13 For the obtained set of coordinates of suspected object
Figure FDA0003770912610000035
Coordinates of the kth suspected target in (1)
Figure FDA0003770912610000036
At the current frame image I t To Chinese
Figure FDA0003770912610000037
Extracting an image block by taking a pixel point at the position as a center, carrying out edge detection on the image block, extracting the edge shape of the suspected target k, and calculating to obtain an image slice formed by pixels surrounded by the edge shape of the suspected target k;
14 ) if the size of the image slice formed by the pixels surrounded by the edge shape of the suspected target k obtained in the step 13) is not in the prior size range of the target, the suspected target k is selected from the suspected target coordinate set
Figure FDA0003770912610000038
Removing the image slices obtained in the step 13) as the image slices of the suspected target k
Figure FDA0003770912610000039
4. The method for detecting and tracking infrared weak and small objects based on the mask and adaptive filtering as claimed in claim 3, wherein the size of the image block in step 13) is greater than 1.25-1.7 times the maximum size of the object and less than 2 times the maximum size of the object.
5. The method for detecting and tracking infrared weak and small target based on mask and adaptive filtering as claimed in claim 2, wherein said step 3) is performed from the previous frame of image I t-1 Extracting a plurality of candidate matching targets corresponding to the suspected target k to form a candidate matching target set, and obtaining a picture slice of each candidate matching target
Figure FDA0003770912610000041
The method specifically comprises the following steps:
31 In the previous frame of image I) t-1 The same as in (1)
Figure FDA0003770912610000042
Selecting an image block with the size of qxq as a target candidate region by taking a pixel point at the position as a center;
32 In combination with the mask set M { M × n }, from the target candidate region, selecting elements whose target types corresponding to the elements in the mask set are not background targets as candidate matching targets to form a candidate matching target set
Figure FDA0003770912610000043
Wherein, J k The number of candidate matching targets in a target candidate area corresponding to the suspected target k;
33 Obtaining an image slice for each candidate matching object based on information of the feature vector M { i } {3} in the mask set M { M × n }
Figure FDA0003770912610000044
6. The method for detecting and tracking the infrared dim target based on the mask and the adaptive filtering as claimed in claim 5, wherein the value range in step 31) is as follows:
3v·Δt/r≤q≤5v·Δt/r
wherein r is the image resolution, v is the maximum motion speed of the target, and Δ t is the time difference between adjacent frame images.
7. The method for detecting and tracking the infrared weak and small target based on the mask and the adaptive filtering as claimed in claim 2, wherein the method for determining the matching coefficient in step 5) specifically comprises:
Figure FDA0003770912610000045
wherein alpha is a filter difference coefficient, and the value range of alpha is 0.01-0.2;
Figure FDA0003770912610000046
for adaptive filters DF k Slicing the suspected target image
Figure FDA0003770912610000047
As a result of the calculation of (a),
Figure FDA0003770912610000048
for adaptive filters DF k Slicing candidate matching target picture
Figure FDA0003770912610000049
The calculation result of (2);
Figure FDA00037709126100000410
and
Figure FDA00037709126100000411
matching targets for candidates
Figure FDA00037709126100000412
In the current frame image I t In the row direction and in the column direction;
Figure FDA00037709126100000413
the coordinates of the kth suspected target.
8. The method as claimed in claim 7, wherein the method for detecting and tracking infrared dim target based on mask and adaptive filtering is characterized in that
Figure FDA0003770912610000051
And
Figure FDA0003770912610000052
the determination method specifically comprises the following steps:
Figure FDA0003770912610000053
wherein,
Figure FDA0003770912610000054
matching targets for candidates
Figure FDA0003770912610000055
Last frame image I t-1 At, the time interval between two adjacent frames,
Figure FDA0003770912610000056
for candidate matching targets obtained from the set of masks M { M × n }
Figure FDA0003770912610000057
The speed of movement of (a) is,
Figure FDA0003770912610000058
for candidate matching targets obtained from the set of masks M { M × n }
Figure FDA0003770912610000059
The angle to the image line direction.
9. The method for detecting and tracking infrared dim target based on mask and adaptive filtering as claimed in claim 2, wherein said step 6) obtains current frame image I t Corresponding background image
Figure FDA00037709126100000510
The method specifically comprises the following steps:
Figure FDA00037709126100000511
wherein, lambda is a background updating coefficient, and the value range of lambda is 0.7-0.9;
Figure FDA00037709126100000512
representing the current frame image I according to the mask set t The pixel values of the pixel points of which the corresponding target types are background or interference targets are kept unchanged, and the pixel values of the other pixel points are processed to zero to obtain a pixel matrix;
Figure FDA00037709126100000513
is shown in the current frame image I t Taking pixel blocks corresponding to the suspected target and the real target as a replaced area, replacing the whole replaced area with the pixel block of the neighborhood background, and according to a mask set, replacing the current frame image I t The pixel matrix is obtained after the zero setting processing of the pixels of the pixel points of which the corresponding target types are the background and the interference target; the value of each pixel in the pixel blocks of the neighborhood background is equal to the average value of the corresponding pixels in the pixel blocks with equal size in four directions of upper, lower, left and right outside the replaced area.
10. The method for detecting and tracking the infrared weak and small target based on the mask and the adaptive filtering according to any one of claims 1 to 9, wherein the step 7) is a method for judging whether the suspected target is a real target or an interference target, specifically: for the elements of which the feature vector target type in the mask set is a suspected target, when the number of times that the target appears in the whole image sequence exceeds Num d Or the target track stops updating beyond Num o Then, calculating the length of the complete track set of the target of the corresponding position of the element, and if the track length exceeds Thr track (ii) a If so, judging the target type of the element as a real target, otherwise, judging the element as an interference target; num d The value range of the image frames is equal to the number of the image frames corresponding to 5 to 10 s; num o The value range of (a) is equal to the number of image frames corresponding to 10-15 s; thr (Thr) track The value range is 3-5.
11. The method for detecting and tracking the infrared dim target based on the mask and the adaptive filtering according to any one of claims 1 to 9, wherein the step 8) is a method for performing track association, and specifically comprises the following steps:
when the track corresponding to the element with the target type being the real target in the mask set stops updating and exceeds Num o When the frame is in use, judging that the real target motion is finished, and adding a target number and a Track into a Track set Track;
when a new track is added into the track set, the track is predicted according to the movement speed and direction of the ending track point of the known track in the track set to obtain the predicted position of the known track in the corresponding new track, and the predicted position is compared with the movement speed and direction of the target corresponding to the initial position of the new track and the new and old tracks to complete the complete tracking of the target; num o The value range of (a) is equal to the number of image frames corresponding to 10-15 s.
12. The method for detecting and tracking the infrared dim target based on the mask and the adaptive filtering according to any one of claims 1 to 9, characterized in that: initial background image
Figure FDA0003770912610000061
The pixel value of each pixel in the image is equal to the average value of pixel points at corresponding positions in the initially obtained N frames of images, and the value range of N is 5-10.
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