CN106447685B - A kind of infrared track method - Google Patents

A kind of infrared track method Download PDF

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CN106447685B
CN106447685B CN201610803370.8A CN201610803370A CN106447685B CN 106447685 B CN106447685 B CN 106447685B CN 201610803370 A CN201610803370 A CN 201610803370A CN 106447685 B CN106447685 B CN 106447685B
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point
template
target
edge
frame image
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CN106447685A (en
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周云
杨皓然
侯森林
闫相宏
吕坚
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image

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Abstract

The infrared track method that the embodiment of the invention discloses a kind of based on Hausdorff apart from matched adaptive template, steps are as follows: firstly, carrying out edge detection using infrared image of the improved sobel edge detection operator to input, extracting the contour feature of target；Secondly, carrying out template matching to target template and region to be searched using quick two-way Hausdorff distance method in tracking；Finally, carrying out adaptive template renewal to optimal matching result.The present invention can for a long time stablize infrared target, precisely track, and arithmetic speed is fast, have compared with hard real-time and robustness.

Description

A kind of infrared track method
Technical field
The present invention relates to digital image processing fields, are based on Hao Siduofu (Hausdorff) distance more particularly, to one kind The infrared track method of matched adaptive template.
Background technique
Nowadays, with the continuous promotion of information technology and computer performance, more and more people have started to computer Target motion problems in vision are studied.In infrared image processing field, infrared object tracking intelligent security-protecting and monitoring with And the rapid development of infrared guidance technology research field, the exigent prison especially in the real-time of some tracking and precision In control and operational environment, there is great meaning to the research of new algorithm.
Infrared object tracking is in Engineering Control, traffic monitoring, medical image research, automated navigation system, astronomical monitoring etc. There is critically important practical value in field.Especially in military aspect, infrared guidance become more and more important main battle weaponry it One.There is also many shortcomings for the algorithm of current most of infrared object trackings, and more complicated algorithm is in real-time target Tracking aspect does not reach requirement, single algorithm not can be carried out again it is steady in a long-term track, this is for modernizing information war Striving is totally unfavorable influence, so the current people research that the infrared object tracking algorithm of research real-time high-efficiency becomes is important Project.
It apart from matched infrared object tracking algorithm is that contour feature based on infrared target carries out mesh based on Hausdorff Mark modeling can be very good specific target appearance feature to carry out robust tracking, add adaptive target template more Newly, influence of the interference to tracking of noise can effectively be reduced.But when current infrared target be blocked, dimensional variation the problems such as Also need deeper into research.
Summary of the invention
The object of the present invention is to provide a kind of infrared track sides based on Hausdorff apart from matched adaptive template Method, tracking stabilization good for infrared object tracking real-time and strong robustness.
Technical solution disclosed by the invention are as follows: using improved Sobel edge detection operator to the infrared image of input into Row edge detection extracts the contour feature of target, in tracking using quick two-way Hausdorff distance method to target mould Plate and region to be searched carry out template matching, adaptive template renewal is carried out to optimal matching result to carry out target with Track, the specific steps are as follows:
Step 1: input infrared video carries out artificial selected target region to initial frame, using improved Sobel operator into Row binary conversion treatment obtains the edge contour information of target and establishes target template；
Step 2: next frame image is read, the Sobel operator that region to be detected improves is handled, after binaryzation, Obtain edge contour information；
Step 3: to the target template and the two-way Hausdorff distance that improves of region to be detected after Sobel Matching, finds out the position of best match；
Step 4: judging the update of target template using adaptive mode slat element, target template is updated if meeting condition And enter next frame, target template is not otherwise updated and enters next frame until Infrared video image terminates.
Further, binary conversion treatment is carried out using improved Sobel operator in step 1, i.e., by edge pixel gray value It is set as 1, rest of pixels value is set as 0, obtains the edge contour information of target and establishes target template, specific as follows:
To the Sobel edge detection process that the infrared image gray value f (i, j) of input is improved, wherein i, j points become Image pixel cross, ordinate value, Sobel operator is the edge detection operator of first derivative, and formula is as follows:
Sx=(Z1+2Z2+Z3)-(Z7+2Z8+Z9)
Sy=(Z1+2Z4+Z7)-(Z3+2Z6+Z9)
Wherein, Z1,Z2,Z3,Z4,Z5,Z6,Z7,Z8,Z9The 8 neighborhood territory pixel gray values of respectively pixel f (i, j), Sx,SyPoint It Wei not horizontal, vertical direction gradient magnitude.
The gradient that improved Sobel operator increases positive and negative 45 ° of directions calculates, and formula is as follows:
S45°=(Z2+2Z3+Z6)-(Z4+2Z7+Z8)
S-45°=(Z2+2Z1+Z4)-(Z6+2Z8+Z9)
Work as Sx,Sy,S45°,S-45°In any one be greater than preset threshold value T when for be edge, record the side of target area The coordinate value of edge pixel establishes target template.
Further, in step 3 to after Sobel target template and region to be detected improve it is two-way Hausdorff distance matching, specific as follows:
To the marginal information set A={ a in target template1,a2,...,amAnd region candidate template to be measured edge letter Cease set B={ b1,b2,...,bnTwo-way Hausdorff distance matching is carried out, formula is as follows:
H (A, B)=max (h (A, B), h (B, A))
Wherein, a1,a2,...,amFor the coordinate information of target template edge aggregation, b1,b2,...,bmFor candidate mould to be measured The edge coordinate of plate,
H (A, B)=max (a ∈ A) min (b ∈ B) | | a-b | |
H (B, A)=max (b ∈ B) min (a ∈ A) | | b-a | |
H (A, B) indicates the point a from set A in above formula (6)iTo the point that nearest set B is put apart from this distance set into Row sequence, takes maximum value therein, and h (B, A) indicates the point b from set B in above formulaiTo the point for putting nearest set A apart from this Distance set be ranked up, take maximum value therein, | | | | be apart from normal form.
When the Hausdorff improved is apart from matching primitives, to current point ai8 contiguous ranges in look for another set Whether the point of this range is had in coordinate, if aiFour neighborhood positions up and down correspond in set B and have at least one corresponding point, Then calculate thisiPoint is 1 to set B point Hausdorff distance, otherwise continually looks for aiEight neighborhood point, which corresponds in set B, to be had accordingly At least one point, then calculate thisiPoint is 2 to set B point Hausdorff distance, and otherwise distance is just denoted as 10.
Further, the update that target template is judged using adaptive mode slat element in step 4, detailed process is as follows:
By calculating the edge point set sum S in target templateAAnd the marginal point of best match position region template Gather sum SB, calculate Adaptive template-updating conditional parameter P1=SA/SBAnd the gray scale of target template region original image Mean value hAWith the original image gray average h of best match position region templateB, calculate Adaptive template-updating conditional parameter P2 =hA/hB, and if only if meeting condition α < P1< β and condition γ < P2When < λ, update current region template is target template, Otherwise it does not update, wherein α, beta, gamma, λ is constant.To reduce the interference of noise, real-time, stable infrared object tracking is carried out.
Detailed description of the invention
Fig. 1 is the stream of the infrared track method based on Hausdorff apart from matched adaptive template of the method for the present invention Cheng Tu.
Fig. 2 is the Sobel filter template schematic diagram both horizontally and vertically of the method for the present invention.
Fig. 3 is the Sobel filter template schematic diagram in positive and negative 45 degree of directions of the method for the present invention.
Specific embodiment
Below in conjunction with attached drawing the present invention will be described in detail method based on Hausdorff apart from matched adaptive template Infrared track method specific implementation process.
As shown in Figure 1, in the method for the present invention, it is a kind of based on Hausdorff apart from matched adaptive template it is infrared with Track method carries out edge detection using infrared image of the improved Sobel edge detection operator to input, extracts the profile of target Feature carries out template to target template and region to be searched using quick two-way Hausdorff distance method in tracking Match, adaptive template renewal carried out to carry out target following to optimal matching result, the specific steps are as follows:
Step 1: input infrared video carries out artificial selected target region to initial frame, using improved Sobel operator into Row binary conversion treatment obtains the edge contour information of target and establishes target template；
Step 2: next frame image is read, the Sobel operator that region to be detected improves is handled, after binaryzation, Obtain edge contour information；
Step 3: to the target template and the two-way Hausdorff distance that improves of region to be detected after Sobel Matching, finds out the position of best match；
Step 4: judging the update of target template using adaptive mode slat element, target template is updated if meeting condition And enter next frame, target template is not otherwise updated and enters next frame until Infrared video image terminates.
Further, binary conversion treatment is carried out using improved Sobel operator in step 1, i.e., by edge pixel gray value It is set as 1, rest of pixels value is set as 0, obtains the edge contour information of target and establishes target template, specific as follows:
To the Sobel edge detection process that the infrared image gray value f (i, j) of input is improved, wherein i, j points become Image pixel cross, ordinate value, Sobel operator is the edge detection operator of first derivative, and formula is as follows:
Sx=(Z1+2Z2+Z3)-(Z7+2Z8+Z9)
Sy=(Z1+2Z4+Z7)-(Z3+2Z6+Z9)
Wherein, Z1,Z2,Z3,Z4,Z5,Z6,Z7,Z8,Z9The 8 neighborhood territory pixel gray values of respectively pixel f (i, j), Sx,SyPoint It Wei not horizontal, vertical direction gradient magnitude.
The gradient that improved Sobel operator increases positive and negative 45 ° of directions calculates, and formula is as follows:
S45°=(Z2+2Z3+Z6)-(Z4+2Z7+Z8)
S-45°=(Z2+2Z1+Z4)-(Z6+2Z8+Z9)
Work as Sx,Sy,S45°,S-45°In any one be greater than preset threshold value T when for be edge, record the side of target area The coordinate value of edge pixel establishes target template.
Further, in step 3 to after Sobel target template and region to be detected improve it is two-way Hausdorff distance matching, specific as follows:
To the marginal information set A={ a in target template1,a2,...,amAnd region candidate template to be measured edge letter Cease set B={ b1,b2,...,bnTwo-way Hausdorff distance matching is carried out, formula is as follows:
H (A, B)=max (h (A, B), h (B, A))
Wherein, a1,a2,...,amFor the coordinate information of target template edge aggregation, b1,b2,...,bmFor candidate mould to be measured The edge coordinate of plate,
H (A, B)=max (a ∈ A) min (b ∈ B) | | a-b | |
H (B, A)=max (b ∈ B) min (a ∈ A) | | b-a | |
H (A, B) indicates the point a from set A in above formulaiDistance set to the point for putting nearest set B apart from this carries out Sequence, takes maximum value therein, and h (B, A) indicates the point b from set B in above formulaiTo the point for putting nearest set A apart from this Distance set is ranked up, and takes maximum value therein, | | | | it is apart from normal form.
When the Hausdorff improved is apart from matching primitives, to current point ai8 contiguous ranges in look for another set Whether the point of this range is had in coordinate, if aiFour neighborhood positions up and down correspond in set B and have at least one corresponding point, Then calculate thisiPoint is 1 to set B point Hausdorff distance, otherwise continually looks for aiEight neighborhood point, which corresponds in set B, to be had accordingly At least one point, then calculate thisiPoint is 2 to set B point Hausdorff distance, and otherwise distance is just denoted as 10.
Further, the update that target template is judged using adaptive mode slat element in step 4, detailed process is as follows:
By calculating the edge point set sum S in target templateAAnd the marginal point of best match position region template Gather sum SB, calculate Adaptive template-updating conditional parameter P1=SA/SBAnd the gray scale of target template region original image Mean value hAWith the original image gray average h of best match position region templateB, calculate Adaptive template-updating conditional parameter P2 =hA/hB, and if only if meeting condition α < P1< β and condition γ < P2When < λ, update current region template is target template, Otherwise it does not update, wherein α, beta, gamma, λ is constant.To reduce the interference of noise, real-time, stable infrared object tracking is carried out.
As shown in Fig. 2, in the method for the present invention, it is a kind of based on Hausdorff apart from matched adaptive template it is infrared with Track method carries out the detection of objective contour using Sobel operator filtering template both horizontally and vertically and establishes contour mould, Formula is as follows:
Horizontal gradient amplitude Sx=(Z1+2Z2+Z3)-(Z7+2Z8+Z9)
Vertical gradient amplitude Sy=(Z1+2Z4+Z7)-(Z3+2Z6+Z9)
Wherein, Z1,Z2,Z3,Z4,Z5,Z6,Z7,Z8,Z9The 8 neighborhood territory pixel gray values of respectively pixel f (i, j).
As shown in figure 3, in the method for the present invention, it is a kind of based on Hausdorff apart from matched adaptive template it is infrared with Track method carries out the detection of objective contour using the Sobel operator filtering template in positive and negative 45 degree of directions and establishes contour mould, public Formula is as follows:
Positive 45 degree of direction gradient amplitudes S45°=(Z2+2Z3+Z6)-(Z4+2Z7+Z8)
Minus 45 degree of direction gradient amplitudes S-45°=(Z2+2Z1+Z4)-(Z6+2Z8+Z9)
In short, in the present invention: the infrared track method based on Hausdorff apart from matched adaptive template, which uses, to be changed Into Sobel edge detection template, enhance the ability of infrared target edge detection；Improved Hausdorff distance is used Matching process greatly improves the operating rate of algorithm；Adaptive template-updating strategy increases the robustness of target following By force, the precision of tracking is improved.

Claims (6)

1. a kind of infrared track method characterized by comprising
Step 1: input infrared video, the selected target region in the initial frame image of the infrared video, and to described initial Frame image carries out binary conversion treatment, obtains the edge contour information of target and establishes target template；
Step 2: reading next frame image, binary conversion treatment is carried out to the next frame image, obtains the edge of area to be tested Profile information；
Step 3: the target template and the area to be tested being matched, the position of best match is found out；
The two-way Hausdorff distance matching that the target template and the area to be tested are improved；
To the marginal information set B=of marginal information set A={ a1, a2 ..., am } and region to be measured in target template { b1, b2 ..., bn } carries out two-way Hausdorff distance matching, and formula is as follows:
H (A, B)=max (h (A, B), h (B, A))
Wherein, a1, a2 ..., am are the coordinate information of target template edge aggregation, and b1, b2 ..., bm are candidate template to be measured Edge coordinate,
H (A, B)=max (a ∈ A) min (b ∈ B) | | a-b | |
H (B, A)=max (b ∈ B) min (a ∈ A) | | b-a | |
H (A, B) expression is arranged from the distance set of point ai to the point for putting nearest set B apart from this of set A in formula (6) Sequence takes maximum value therein, in formula (7) h (B, A) indicate from the point bi of set B to the point for putting nearest set A apart from this away from It is ranked up from set, takes maximum value therein, | | | | it is apart from normal form；
When the Hausdorff improved is apart from matching primitives, to being looked in another set B in 8 contiguous ranges of current point ai Whether point positioned at this range is had, if four neighborhood positions up and down of ai, which correspond in set B, at least one corresponding point, This ai point is calculated to set B point Hausdorff distance as 1, otherwise continually look for other upper lefts of ai eight neighborhood point, lower-left, upper right, Whether corresponding at least one point is had in four neighbor assignment set B of bottom right, if so, then calculating this ai point to set B point Hausdorff distance is 2, and otherwise distance is just denoted as 10；
Step 4: judging whether target template meets update condition using adaptive mode slat element, if meeting update condition more Fresh target template simultaneously reads next frame image execution step 1 to step 4, does not otherwise update target template and reads next frame image Step 1 is executed to step 4, until all images in the infrared video are all disposed.
2. the method as described in claim 1, which is characterized in that the step 1 includes: using improved Sobel operator to institute It states initial frame image and carries out binary conversion treatment, set 1 for the edge pixel gray value of the target area, rest of pixels value is set It is 0, obtains the edge contour information of target and establish target template.
3. method according to claim 2, which is characterized in that the step 1 includes:
For each pixel in the initial frame image, calculate separately:
Sx=(Z1+2Z2+Z3)-(Z7+2Z8+Z9)
Sy=(Z1+2Z4+Z7)-(Z3+2Z6+Z9)
S45 °=(Z2+2Z3+Z6)-(Z4+2Z7+Z8)
S-45 °=(Z2+2Z1+Z4)-(Z6+2Z8+Z9)
Wherein, Z1,Z2,Z3,Z4,Z5,Z6,Z7,Z8,Z9Respectively 8 neighborhood territory pixel gray values of current pixel point；
As the S of current pixel pointx,Sy,S45°,S-45°In any one be greater than preset threshold value when, current pixel point be edge picture Element.
4. the method as described in claim 1, which is characterized in that the step 2 includes: using improved Sobel operator to institute It states next frame image and carries out binary conversion treatment, set 1 for the edge pixel gray value in the next frame image, rest of pixels Value is set as 0, obtains the edge contour information of area to be tested.
5. method as claimed in claim 4, which is characterized in that the step 2 includes:
For each pixel in the next frame image, calculate separately.
6. the method as described in claim 1, which is characterized in that step 4 includes:
By calculating the edge point set sum S in target templateAAnd the edge point set of best match position region template is total Number SB, calculate Adaptive template-updating conditional parameter
P1=SA/SB, and pass through the gray average h of target template region original imageAWith best match position region template Original image gray average hB, calculate Adaptive template-updating conditional parameter P2=hA/hB, and if only if meeting condition α < P1< β With condition γ < P2When < λ, update current region template is target template, is not otherwise updated, wherein α, beta, gamma, λ is constant.
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