Accurate Tracking Based on Interest Pointsin the Infrared Imaging Guidance

Wei Lin Huimin Ma Fengting LiDepartment of Electronic Engineering

Tsinghua UniversityBeijing 100084, China

E-mail: woodcheaper@sohu.commhmpub@tsinghua.edu.cnlift@tsinghua.edu.cn

Abstract-Nowadays, infrared imaging guidance has becomeone of the most magistral guidance technologies. And theterminal accurate tracking technology has been a study hotspotand attracted many scholars in different countries round theworld. Up to now, the current approaches to terminal trackingwidely used in infrared imaging guidance missiles mainly baseon the anterior cusp of the battleplane. This paper develops anew approach to perform the terminal accurate tracking,which bases on the Interest Points. Interest Points are thepoints around which the intensity varies acutely and typically.In this paper, we compute these Interest Points from areformative Laplacian of the false color infrared image, andthen use them to form a tracking point. A simulation system isbuilt to test this approach and the result shows the newtracking approach based on Interest Points can perform aquite accurate terminal guidance.

I. INRODUCTION

Target tracking is now being more and more widelyapplied in the military domain. And those weapons whichcan perform automatic and accurate tracking now become afavourite son, for their excellent performance to attack themobile target.Of all the accurate guidance weapons, the infrared

imaging guidance missile is being used most widely in themodem war to attack the tank and the battleplane.Compared with the other guidance technologies, the infraredimaging guidance has the advantage of high accuracy, widedynamic range, great capability of concealment andanti-jamming, automatic capture and decision-making, andso on[].

Infrared guidance technology has been chronicallydeveloping since last century. At the beginning, the missilecan only get an image with a blurry point in it. Then therecomes the missile which can acquire a binary image toperform the guidance. Later on, the technologies using theinfrared gray image are developed. With more local features,the infrared gray image can provide a much better trackingcapability. In the recent few years, some of the newantiaircraft missiles are beginning to use the false colorinfrared image in the guidance, which makes use of the

typical, unique intensity features of the plane's infraredimages. For most of the infrared imaging guidance missilesnowadays, the binary image and the gray image are still themagistral approaches. However, the false color image haspresented its great practical potential.

In order to recce and lock the target over ten kilometersfar away, the instantaneous visual angle of the missile isvery small, usually about 3°. So in the terminal tracking, thetarget will slop over the field of vision. The missile'sinfrared detector is then no more able to see the whole targetand, what it can make use of is just only a part of the target.In this case, the missile must focus a local point to performthe terminal tracking. And for the antiaircraft missile, themost frequently used point now is the anterior cusp of thebattleplane. This method has some disadvantages, whichwill be detailedly discussed in the following sura. This papermainly talks about a new approach to form the trackingpoint based on Interest Points in the false color infraredimage, explain the advantage of this new approach, and testthe research through a simulation system.

IL TRACKING BASED ON THE ANTERIOR CUSP

The terminal tracking based on the local point can begenerally named the Interest Point Tracking. That is becausethe anterior cusp of the battleplane is virtually a InterestPoint. In the infrared image of the battleplane target, thiscusp is so striking and so easy to extract, and in lots of cases,it can be of good stabilization.

Fig.1 Plane's Anterior Cusp as the Tracking Point

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When we observe the anterior cusp of a plane fromdifferent viewpoints, we can find different appearances of it.One of the typical appearances of the anterior cusp of thebattleplane is shown in fig.1.

In Fig. 1, we can see that the anterior cusp is of moreinerratic geometrical characteristic compared with othergeometric angular vertexes such as the cusps of airfoils andempennages. In other words, it is a big angular vertex of theplane. That's why most of the antiaircraft missiles use thiscusp to perform the terminal tracking.

However, there are two insuperable limitations for thisapproach.

First, the anterior cusp is a geometric angular vertex at theoutline of the plane, about several meters away from thecentroid. And it's quite small relative to the whole fuselage,which means, if the missile focus the cusp, the probability ofbeing dead on the target is very low.

Secondly, the anterior cusp is not steady enough for thetracking. In some certain regions of the viewpoint, this cuspcannot be detected. Experimentally, we find that in the tapersolid angle of 200 forward and 30° backward shown in Fig.2,the anterior cusp is lost at the outline. And Fig.3 shows theblind area backward, in which the anterior cusp is occludedby the fuselage.

i/ 1. I...;- I

t ....k 4. _e/-

- IW1II IitEI{ J. I, a.

Fig.2 Blind Area of the Anterior Cusp Detection

Fig.3 Blind Area Backward

Besides these two blind areas, the anterior cusp may alsobe occluded by other parts of the plane such as the airfoils orempennages. That's why the existing approach using theanterior cusp loses the target ever and agah during thetracking.

III. TRACKING BASED ON INTEREST POINTS

image can be easily and approximately found by eye, can bewell detected through some certain algorithms and, can beused to describe the features of the target so as to performthe tracking or recognition. Different algorithms detectdifferent kind of Interest Points. For example, we can detectthe angular vertexes of the plane using the SUSAN operator.We can also compute the extremum of the image or thecentroid of the target. All these points can be calledgeneralized Interest Points. Using these points, we've gotthe extremum point tracking, centroid tracking, correlationtracking and so on [2].When we talk about the Interest Point tracking, it means

the tracking based on the special Interest Points aroundwhich the intensity varies acutely and typically [3]. Here,special Interest Points are defmed according to the localintensity variety. Different from the angular vertexes,extremum point and centroid, the special Interest Pointsrequire abundant detail variety in the image. And the falsecolor infrared image meets the case. It has three componentsand provides nmore local variety information than the grayimage. The unborn antiaircraft missiles will largely dependon the false color infrared image to perform the guidance.So it is of great significance to study the false color infraredimage and the Interest Points in it. In the following chapters,when we refer to the special Interest Points, we use InterestPoints for short.

The false color infrared image uses different color hues torepresent different temperatures. Fig.4 shows theapproximate curve between the temperature and the color.

A L Intensity

I B G R

Temperature

Fig.4 The Curve Between the Temperature and the Color in theFalse Color Infrared Image

According to the curve above, the infrared detectortranslate the infrared ray signal to a false color image. Thetypical false color infrared images of battleplanes withdifferent velocities are shown in Fig.5. We can see fromFig.5 that different velocities bring about different hues. Butno matter in what case, the typical features is uniform. Thetemperature at the engine is always the highest, whichappears red or crimson in the false color infrared image.And from the anterior fuselage to the engine there is anobvious saltation of the temperature. In the false colorinfrared image, we can describe this saltation with twocurves, of which one is the Green curve and the other one isthe Red curve.

Interest Points refer to the points of distinct token featuresin the target image. In exoteric words, Interest Points in the

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(a) bD)

(c) (d)

Fig.5 The False Color Infiared Images of Battleplanes withDifferent Velocity (Va< Vb < Vc <Vd)

Furthermore, the region where the saltation takes placecontains really abundant local information of the intensityvariety. Therefore, we can detect the Interest Points in thisregion for the terminal tracking. There are three gratifyingadvantages of this approach. First, the Interest Points in thatregion are steady and invariable. When the viewpointchanged, their amount and position vary tardily. So, it isable to effectively perform the accurate tracking using theInterest Points. Secondly, the region lies between theanterior fuselage and the engine, a bit wider than the headand the afterbody. They can be detected almost from everyviewpoint so it is less probable to lose the target. Thirdly,the Interest Points are near the engine and a completedestroy will bring on if the missile is dead on the target.

Fig.6 shows the result of computing the Interest Pointsfrom the false color infrared image. The pixels of greencolor around the engine region are no other than the InterestPoints we need.

Fig.6 The Interest Points of the Battleplane

The Interest Points are located at extrema of theLaplacian of the image [4][5]. This function is chosen for its

response at points with 2-dimensional structure. To find themaxima and minima of the Laplacian we first use theGaussian as a lowpass to smooth the image. Then wecompute the Laplacian function of the image using thefollowing expressions

LR kR -&T + 0l)

LG=kG\1

iG + ay)

LB = B ( |+|ay2|where R, G, B are the three color components and LR, LG, LBare the multiple approximations of the Laplacian with theplus coefficients kR, kG, kB. The multiple approximations areof extraordinary sensitivity and they can effectively detectthe points around which the intensity of the colorcomponents varies tinily. And then we set appropriatethresholds of the three Laplacians to eliminate what wedon't need and hold what we need. In the end, the red,yellow, white pixels are all eliminated and some of the greenpixels remain, which are named Interest Points, the specialInterest Points. We compute the centroid of the InterestPoints and aim at the centroid to perform the accurateterminal tracking.

IV. SIMULATION TEST

In order to test the effect of the tracking based on InterestPoints, we build a simulation system with C++. The systemis a piece of mathematical simulation software with fullfunctions, and of excellent performance. The systemconsists of the model creating module, the 3-D drawingmodule, the tracking module, the recognition module, thecourse control module, and the user dialog module. And thissystem can be used to simulate the whole process of infraredimaging guidance, test the tracking algorithms, andeffectively evaluate the application of the tracking algorithmbased on the Interest Points in the infrared imagingguidance.

In this simulation system, 3-D models of some of thefamiliar battleplanes and one kind of infrared imagingguidance missile are created. Then we use the projectiontransformation of OpenGL to factually simulate the captureof the target's false color infrared image. What the trackingmodule does is absolutely based on the image captured. Thetracking module must analyse the false color infrared image,pretreat the image, then detect the tracking point, adjust themissile's pose to lock the target and ensure the pursuit. Atthe end of the tracking, the tracking point is the centroid ofthe Interest Points. Whether the missile can lock the targetor not, completely depends on the capability of thealgorithm in the tracking module.

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For the Interest Point tracking, we need to fix the pluscoefficients and the thresholds of the Laplacians. Differentvalues of the plus coefficients result in different amount anddifferent intensity of the Interest Points. After experimentstimes without number, we find that kR = kG = kB = 100 servesmost battleplanes' infrared images very well. We alsocompute the PR, PG, PB, which symbolize the thresholds ofLR, LG, LB. And, we've got 0.1, 0.6, and 0.3. Therefore, theInterest Point detection expression can be presented asbelow.

L Il,l LR 0.1 andLG> 0.6 and LB< 0.3

t0 othersThe pixels where L = 1 are the Interest Points we need.

The test result shows the approach can be used at the end ofthe guidance to perform an accurate tracking.

Concretely, the simulation system adopts the order belowto carry out the guidance: first the point tracking, then thecentroid tracking, the correlation tracking, and finally theInterest Point tracking. And the missile changes its samplingfrequency as follows: 16 Hz while scanning and scouting,50 Hz while pursuing, and 100 Hz while intercepting thetarget. The simulation result gives an average of theguidance accuracy error. The detonating distance betweenthe seeker and the plane's centroid is limited to 5 meter evenconsidering the bulk of the plane and the restriction of themissile's flexibility, and the missdistance is smaller than 1meter. The Table I below shows the simulation result of theguidance accuracy errors for 4 real battleplanes. The valuesof the detonating distance and the missdistance are theaverage of repetitious tests with the unit of meter.

TABLE I

SIMULATION RESULT

Plane No.1 No.2 No.3 No.4

Length 19.10 19.43 15.09 20.08Height 4.88 5.63 5.09 3.78

Wingspan 11.65-19.54 13.05 9.45 13.2

Detonating 3.36 3.79 1.86 4.21distance 0.644___42_0_34 _0_39Missdistance 0.644 0.42 0.34 0.38

of using the anterior cusp to perform the tracking. Then wecommence with the false color infrared image, analyze thecharacteristic of the battleplane's infrared image, expoundthe Interest Point theory, set concrete parameters for thisproblem, and then detect the Interest Points to perform theterminal tracking. The simulation result proves that the newapproach is of quite high performance to achieve thezero-missdistance approximatively. As the case stands, thisapproach can still be improved on so as to make a highercomputing efficiency and tracking performance. Forexample, reformative scale-space Laplacian [6], moremultiple coefficients, or advanced guidance laws will do agreat deal of good to the Interest Point Tracking. Furtherwork will be done to develop the level of the infraredimaging guidance.

ACKNOWLEDGMENT

The simulation system was written in part by Mr. HuYichuan. And we would like to thank Mr. Luo Yuan for hisvaluable suggestions and discussions.

REFERENCES

[1] Feng Chitao, "A Review on the Development of IR GuidanceTechniques", Infrared Technology, vol. 16(2), pp. 1-4, Mar. 1994.

[2] Jiang Pengyue, "Analysis of Target Tracking Algorithms", FireControl & Command Control, vol. 23(12), pp. 47-52, Jun. 1998.

[3] David G. Lowe, "Local Feature View Clustering for 3D ObjectRecognition", Proceeding of the IEEE Conference on ComputerVision and Pattern Recognition, Kauai, Hawaii, Dec. 2001, vol.1, pp.682 - 688.

[4] Cordelia Schmid and David Lowe, "Recognition and Matching basedon local invariant features", Invited Report of the IEEE Conferenceon Computer Vision and Pattern Recognition, Madison, WI. Jun.2003.

[5] Matthew Brown and David Lowe, "Invariant Features from InterestPoint Groups", Proceedings of the 13th British Machine VisionConference, University of Cardiff, UK, Sept. 2002, pp. 253-262.

[6] P. Burt and E. Adelson, "The Laplacian Pyramid as a CompactImage Code", IEEE Transactions on Communications, Vol 9, No. 4,pp. 532-540, 1983.

From the result, we can see that the missile assuredlyperforms an accurate tracking, of a large probability to bedead on the target and cause a completely severe destroy.

V. CONCLUSIONS

This paper mainly presents a new approach to perform theterminal tracking based on Interest Points in the infraredimaging guidance, and use a simulation system to test theaccuracy of this approach. Basing on the analysis of theexisting approaches, we list the two insuperable limitations

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