ABSTRACT 1. INTRODUCTION

Transcription

1 Arborne Target Trackng Algorthm aganst Oppressve Decoys n Infrared Imagery Xechang Sun, Tanxu Zhang State Key Laboratory for Multspectral Informaton Processng Technologes; Insttute for Pattern Recognton and Artfcal Intellgence, Huazhong Unversty of Scence and Technology, Wuhan , Chna ABSTRACT Ths paper presents an approach for trackng arborne target aganst oppressve nfrared decoys. Oppressve decoy lures nfrared guded mssle by ts hgh nfrared radaton. Tradtonal trackng algorthms have degraded stablty even come to trackng falure when arborne target contnuously throw out many decoys. The proposed approach frst determnes an adaptve trackng wndow. The center of the trackng wndow s set at a predcted target poston whch s computed based on unform moton model. Dfferent strateges are appled for determnaton of trackng wndow sze accordng to target state. The mage wthn trackng wndow s segmented and mult features of canddate targets are extracted. The most smlar canddate target s assocated to the trackng target by usng a decson functon, whch calculates a weghted sum of normalzed feature dfferences between two comparable targets. Integrated ntensty rato of assocaton target and trackng target, and target centrod are examned to estmate target state n the presence of decoys. The trackng ablty and robustness of proposed approach has been valdated by processng avalable real-world and smulated nfrared mage sequences contanng arborne targets and oppressve decoys. Keywords: arborne target trackng, oppressve decoy, target state, trackng wndow determnaton, decson functon, nfrared magery 1. INTRODUCTION Target trackng s an mportant research area of computer vson and a key technque n Infrared Search and Track (IRST) systems. Many automatc target trackng algorthms, such as correlaton match, optcal flow method, Kalman flter, partcle flter, mean shft technque, dynamc programmng, are proposed n the open lterature. Arborne target trackng n nfrared magery s crucal n nformaton processng module of nfrared guded mssle. Dfference n nfrared radaton, moton characterstc and shape feature between target and background s the prncpal for target trackng. Many approaches have been developed to solve ths problem 1, 2. An effcent detecton and trackng system based on an nnovatve target trajectory flterng s proposed by Carlos R. del-blanco et al 3. Ther approach compensates global moton frstly, and then potental trajectores are analyzed by curve fttng technque, erratc trajectores whch ndcate false targets are deleted. In [4] arborne plume trackng s addressed by plume predctor modelng and a process detecton based approach, sensor networks s used to deal wth some problems such as low qualty of data. Intensty varaton functon (IVF) whch models target ntensty profle and target shape based template are ncorporated for target trackng n forward-lookng nfrared (FLIR) mage sequences 5. The algorthm s capable of trackng one or mult target. Common algorthms generate acceptable results under the stuaton of tradtonal arborne-countermnng wthout decoys, but ther performance degraded evdently when valuable target uses nfrared decoys. Infrared oppressve decoys emphasze ts own radaton to deceve nfrared guded mssle and ncrease survvablty of valuable target. In [6] a novel PVPI-FJTC system s ntroduced. Potental applcaton of vsble passve magng polarmetry n real target recognton aganst decoys s demonstrated. Hyperspectral sgnature and correspondng transform doman analyss method has proved effectve for dscrmnatng arborne target radaton from decoy flare 7, yet hyperspectral seeker has not been extensvely used n practce. E-mal: sunxechang@tom.com; Telephone: MIPPR 2009: Automatc Target Recognton and Image Analyss, edted by Tanxu Zhang, Bruce Hrsch, Zhguo Cao, Hanqng Lu, Proc. of SPIE Vol. 7495, 74953I 2009 SPIE CCC code: X/09/$18 do: / Proc. of SPIE Vol I-1

2 How to track stably valuable arborne targets when they throw out oppressve decoys s addressed n ths paper. By adaptvely choosng trackng wndow, proposed algorthm can suppress effect of nfrared decoys and avod leakage of valuable target. Then state of valuable target s estmated by usng feature constrant. The approach can keep a stable trackng of arborne target n two specal cases: target s hdden behnd decoys or adjacent to decoys. The trackng ablty of the approach for arborne target trackng has been valdated by expermental results. The outlne of ths paper s as follows. In Secton 2 characterstc of arborne target, decoy and background n nfrared magery s dscussed. In Secton 3 the approach for trackng arborne target aganst oppressve decoys n nfrared mage sequence s descrbed and detaled. The experment results are presented n secton 4. Conclusons are gven n secton CHARACTERISTICS OF AIRBORNE TARGET, DECOY AND BACKGROUND Every object emts nfrared radaton and ths s the man prncple employed by nfrared target detecton, trackng and recognton systems. To the same object, nfrared radaton ntensty s monotoncally related to the object s temperature. Arborne target radaton whch can be captured by nfrared sensors composed of three man parts: plane cover whch s heated by ar-frcton, engne exhaust plume and talng spout heated by plume, whle background radaton comes manly from sky and some surface features. The three parts of arborne target have dfferent temperatures. Usually talng spout s temperature s hgher than plume s and the cover s s the lowest. Background temperature s lower than each part of target, thus background s ntensty s lower than target s, whch s employed to detect arborne target n nfrared magery. When the background s non-textured sky, arborne target can be effectvely dstngushed from background. Ths task becomes more dffcult when much cloud clutter or surface features appear. Often, target features such as ntensty, shape and moton are consdered to enhance target recognton capablty n that case. Infrared decoy has appeared as an effectve countermeasure whch often leads to mssle s falure n target trackng. Characterstcs of nfrared decoys are deeply analyzed and smulated 8, 9. Oppressve decoy lures nfrared guded mssle by ts hgh radaton n nfrared band. Typcally decoy temperature s about 2000K, whle talng spout temperature s about 900K. So decoy radaton ntensty s much hgher than target. Besdes hgh radaton, to mprove survvablty of valuable target, nfrared decoy must have some other characterstcs. Frst, the dstance between decoy and protected target should be mantaned n an approprate range and perod, f the dstance s too large, the purpose of protectng target from beng attacked could not be reached; on the other hand, decoy may ntroduce unexpectable damage to target at a too close dstance. Second, decoy radaton must keep hgh n a perod of tme. Arborne target often contnuously throw out many decoys for msgudng mssle. When nfrared decoys are thrown out, valuable target s hdden behnd or adjacent to decoys n the magery. Then target and decoys separate at a proper speed. The dffcultes of trackng real target arse from the confuson nduced by decoys, especally when real target s occluded by decoys and the deployment of decoys s elaborately desgned accordng to target moton. Trackng algorthms commonly produce degraded stablty even come to trackng falure under that stuaton. 3. AIRBORNE TARGET TRACKING Often, arborne target trackng s trggered by target detecton module when valuable targets are acqured. Target detecton n nfrared magery s a hot research area. Many target detecton algorthms are developed for varous targets. Although target radaton s hgher than background, arborne target detecton suffers from a dm small target because target acquston must be done at a long range. Scene knowledge based algorthms employng ego-moton remove and resdual moton detecton 10 could be appled when target mage has shape nformaton aganst textured background. In our experments we utlze multlevel flter 11 for target detecton, and the proposed trackng approach start up when real targets acquston s completed. Descrpton of the proposed trackng approach (Fg.1) s as follows: Trackng wndow, namely target search area, s determned n current nfrared dgtal mage accordng to target features lke poston, state, moton, and sze, whch are obtaned through trackng hstory. Trackng wndow s enlarged when sensor ego-moton compensaton s not avalable. Segmentaton s mplemented n trackng wndow and canddate targets are extracted. Feature-based Assocaton module uses a decson functon, whch sums normalzed feature dfferences between two comparable targets, to select one Proc. of SPIE Vol I-2

3 canddate target as the assocaton target of the trackng target. Target state s estmated by analyss of ntegrated ntensty rato and centrod poston of trackng target and assocaton target. Target poston s valdated or estmated accordng to estmaton of target state. IR mage sequence Trackng Wndow Determnaton Canddate Target Extracton Trackng Hstory Target State [Target Features] Target Poston Target Valdaton or Estmaton Feature-based Assocaton Fg.1. Overvew of the proposed approach for arborne target trackng. 3.1 Trackng Wndow Determnaton Target state and target features are recorded. There are three states of target whch are Normal, Occluded and Dsappeared. Target state s updated by the approach at the tme of the prevous frame or the current frame. Intal target state s set to Normal. The center of trackng wndow s set at a predcted poston computed by usng a unform moton model. v v v Pt = Pt n + V n (1) where t s the frame number of current mage. P v t represents estmated target poston n the current mage. V v represents estmated velocty of the trackng target. t n s the closest frame number of the mage n whch target state s Normal. The sze of trackng wndow s determned accordng to target state. We apply dfferent strateges to dfferent target states for determnng trackng wndow sze. If target state s Normal, the trackng wndow sze s smply set to target sze plus a margn to suppress the effect of cluttered background and decoys. If target state s Occluded, the trackng wndow s chosen from a seres of canddate trackng wndows by the rule of maxmzaton of entropy, through the rule we hope to choose a trackng wndow whch s proper for extracton of potental targets and decoys. A wndow wth sze l s represented by W() l, The mnmum sze l mn s set to the larger one of target heght and wdth, and the maxmum sze l max = r l mn, n whch r s typcally set to 4. Entropy of W() l s calculated by where represents ntensty value exsts n W () l. satsfes El () = plog (2) p 2 p s the frequency of ntensty n W () l. The chosen sze l 0 Proc. of SPIE Vol I-3

4 E ( l0) E( l) l : lmn l l (3) max If target state s Dsappeared, the approach should acqure valuable target agan, therefore the trackng wndow s the entre mage. 3.2 Canddate Target Extracton The trackng wndow could be partal mage or entre mage dependng on the state and features of the trackng target. If trackng wndow s partal mage or valuable target s bg (we mean that target sze s bgger than 9 9 pxels), OTSU method s appled for segmentaton. When the scene n a trackng wndow contans decoys, a threshold above target ntensty s probably generated usng OTSU method because decoys have much hgher ntenstes than target and occupy a consderable rato of the trackng wndow. In ths case an alternatve threshold should be calculated by applyng OTSU method agan below the above threshold. When trackng wndow s the entre mage and the valuable target (n trackng hstory) s small, a re-detecton of the valuable target s requred. Ths could be done by target detecton module or target trackng module. Multlevel flter 11, whch realzed a band-passed flter, s ncorporated to enhance potental targets and suppress background, fout = ( fn ( fn Lp1* Lp2 *... Lpn ))* Lq1* Lq2 *... Lq (4) m where f n represents the orgnal mage, f out s the fltered result, Lp,1 n and Lq j,1 j m are low-passed flters. * represents convoluton, n and m are both postve ntegers. Intensty statstcs based segmentaton s appled to the fltered mage. Connected components, whch ndcate canddate targets, are labeled n segmentaton result. After that, mult features ncludng centrod, ntensty, area, sze, shape factor and crcularty of canddate targets are extracted. Any canddate target, whose centrod has a smaller dstance to the boundary of the mage than half of the target sze n the orentaton perpendcular to the boundary, s deleted for avodng the stuaton that a canddate target s part of a real component n the scene. Anomaly that there s no canddate target n trackng wndow may exst. In ths case target state s drectly set to Dsappeared whch ndcate that the approach fal to track valuable target n the current mage and turn to process the next mage. 3.3 Feature-based Assocaton To fnd assocaton target, namely the most smlar canddate target wth the trackng target, ths module calculates a value for each canddate target by usng a decson functon. Then the canddate target has smallest value compared to other canddate targets s chosen as assocaton target. The decson functon s a weghted sum of normalzed feature dfferences between two comparable targets (a canddate target and the trackng target), whch can be wrtten as T g (x) = w x (5) where w s weght vector, x s the vector of normalzed dfferences, each component of x s normalzed nto [0 1], assume d features are used, then x and w=[,... ] x=[,..., ] ) represents respectvely a feature dfference and the correspondng weght. w (1 d T w1 w2 w d (6) T x1 x2 x d (7) The value of the decson functon s between 0 and 1. The smaller the functon value s, the more smlar the two comparable targets are. All weghts are obtaned by the method of supervsed learnng on tranng data. Let x represents the normalzed feature dfference vector between assocaton target and the trackng target, then 0 g(x 0 ) g(x) (8) where x s a normalzed feature dfference vector between any canddate target and the trackng target. Proc. of SPIE Vol I-4

5 Trackng hstory s recorded by a chan of nodes and each node contans target features at a certan tme. The trackng target feature used for assocaton could be the latest node of the chan, or computed from mult nodes of the chan accordng to specfc means, such as average of features of mult nodes. 3.4 Target Valdaton or Estmaton Integrated ntensty of assocaton target s used to estmate target s current state. Integrated ntensty s sum of ntenstes of all pxels that the trackng target or canddate target occupes. Integrated ntensty rato of the assocaton target and the trackng target s r = Ia / I (9) t where I and a I represents ntegrated ntensty of assocaton target and the trackng target respectvely. Whether the t assocaton s successful or not s decded by the rule as follows: set a threshold η ( η > 1) for r, f r η, the assocaton s consdered as successful, assocaton target s added to trackng hstory and target state s set to Normal; otherwse the assocaton s consdered as unsuccessful, n ths case the trackng target poston and ntensty s examned. If there s a canddate target coverng target poston and ts ntensty s hgher than the trackng target s, target state s set to Occluded, otherwse target state s set to Dsappeared. 4. EXPERIMENTAL RESULTS The performance of the proposed approach for arborne target trackng s evaluated by usng nfrared mage sequences, most of whch are generated by smulaton algorthms and some are real-world data. These nfrared mage sequences have a 14-bt precson and mage sze s pxels. A varety of stuatons wth varyng target velocty and dfferent decoy events are smulated to evaluate the approach s robustness. Dfferent trackng wndows and correspondng segmentaton results of two orgnal mages wth frame number 335 and 565 are shown n Fg. 2. The two mages are from the same sequence, there are three arborne targets wth specfc range, postures, movements and decoy events. The mddle target s trackng state s Normal when the mage numbered 335 (Fg. 2(a)) s processed, so the trackng wndow sze s set to target sze plus a margn to remove effects of other targets, decoys and background. In Fg. 2(b), the mddle target s partally occluded by a decoy; ts trackng state s Occluded when the mage numbered 565 s processed, therefore the trackng wndow determned by the approach s a relatve larger area ncludng target and decoys. It can be seen that the combnaton of target and a decoy s segmented as a whole component, whch avods partal segmentaton and mprecse feature of canddate target. Fg.2. Trackng wndow and segment result. (a) Target wthout decoy, target state s Normal, (b) Target wth decoys, target state s Occluded. In the experments two gate trackng algorthms 12, edge-based gate trackng algorthm and centrod-nearest gate trackng algorthm, are used as comparatve methods. In an experment ncludng twelve smulated nfrared mage sequences, there are ten sequences contanng nfrared decoys. All three algorthms track stably valuable targets n the sequences wthout decoys. For ten sequences wth nfrared decoys, the rato of successful trackng of the proposed approach s 0.8, whle that of the other two algorthms are less than 0.2. Expermental results show that the proposed algorthm performs more stable n arborne target trackng. Valuable target are correctly trackng or the poston s more precsely estmated under a varety of scene condtons n the presence of mult oppressve decoys wth dfferent movement characterstcs. The other two algorthms usng tradtonal trackng strategy often fal to track real targets when nfrared oppressve Proc. of SPIE Vol I-5

6 decoys are thrown out. They perform successful dscrmnaton of target and decoy when target sze n mage s large enough. Fg.3. Trackng performances of two tradtonal trackng algorthms and the proposed approach under three dfferent stuatons (form left to rght): target wthout decoy, target occluded by decoy and target separated from decoy. (a) Edge-based gate trackng algorthm, (b) Centrod-nearest gate trackng algorthm, (c) The proposed algorthm. The trackng performances of the three algorthms are llustrated n Fg. 3. Three orgnal mages wth frame number 335, 418, 487 n a sequence are shown from left to rght and there are three real targets n the scene. The mddle target s the nterestng target n ths experment. It performs a clmbout and throws out a decoy at the tme of frame number 410. In mage numbered 335 the target s normally tracked wthout decoy; n mage numbered 418 the target s occluded by a decoy; n mage numbered 487 the target has separated from the decoy. Fg. 3(a), 3(b) and 3(c) shows respectvely the trackng results of edge-base gate trackng algorthm, centrod-nearest gate trackng algorthm and the proposed algorthm. When the target throws out a decoy, the proposed algorthm produces a more precse estmaton of target poston than the other two algorthms. After the separaton of the target and decoy, the proposed algorthm performs a stable trackng, whle the other two algorthms are msguded by the decoy. 5. CONCLUSIONS We descrbe an approach for arborne target trackng aganst oppressve decoys n nfrared magery. The approach conssts of trackng wndow determnaton, canddate target extracton, feature-based assocaton and target valdaton or estmaton. By usng an adaptve trackng wndow accordng to target state and target features, the proposed approach Proc. of SPIE Vol I-6

7 can suppresses effect of nfrared decoys and background. Mult-feature based assocaton s followed by ntegrated ntensty analyss, whch decdes whether the assocaton s successful or not. Then target centrod and coverage of canddate targets are examned to estmate trackng state of valuable target. The performance of ths approach has been evaluated on avalable real-world and smulated nfrared mage sequences contanng arborne targets and nfrared decoys. Expermental results show that ths approach yelds acceptable results under a varety of scene condtons n the presence of mult oppressve decoys wth dfferent movement characterstcs. Acknowledgement Ths work s supported by the Project of the Natonal Natural Scence Foundaton of Chna under Grant No and the Project of the Natonal Defense Fundamental Research of Chna under Grant No.A REFERENCE [1] Greg A. Page, Bran D. Carroll, Alan Pratt and Peter N. Randall, Long-Range Target Detecton Algorthms for Infrared Search and Track, Proc. SPIE on Infrared Technology and Applcatons XXV 3698, 48-57(1999). [2] D.J. Clarke and P.N. Randall, InfraRed Search and Track Technology Demonstrator Programme, Proc. SPIE 3061, (1997). [3] Carlos R. del-blanco, Fernando Jaureguzar, Lus Salgado and Narcso Garca, Automatc aeral target detecton and trackng system n arborne FLIR mages based on effcent target trajectory flterng, Proc. SPIE on Automatc Target Recognton XVII 6566, (2007). [4] Glenn T. Nofsnger and George V. Cybenko, Arborne Plume Trackng Wth Sensor Networks, Proc. SPIE on Unattended Ground, Sea, and Ar Sensor Technologes and Applcatons VIII 6231, (2006). [5] A. Bal and M. S. Alam, Automatc Target Trackng n FLIR Image Sequences Usng Intensty Varaton Functon and Template Modelng, IEEE Transactons on Instrumentaton and Measurement 54(5), (2005). [6] Aed M. El-Saba, Potental Applcaton of Vsble Passve Imagng Polarmetry n the Dscrmnaton of Real Targets and Decoys, Proc. SPIE on Optcal Pattern Recognton XVI 5816, (2005). [7] Nahum Gal, Jacob Barhen, Sandeep Gulat, and Capt. Todd D. Stener, Hyperspectral Ar-to-Ar Seeker, Proc. SPIE 2231, (1994). [8] Wm de Jong, Frans A.M. Dam, Gerard J. Kunz, Rc. M.A. Schlejpen, IR seeker smulator and IR scene generaton to evaluate IR decoy effectveness, Proc. SPIE on Technologes for Optcal Countermeasures 5615, (2004). [9] WANG Chao-qun, Some characterstcs of nfrared jam and ts smulaton technque on nfrared guded mssle, Infrared and Laser Engneerng 30(4), (2001). [10] A. Strehl and J. K. Aggarwal, Detectng movng objects n arborne forward lookng nfra-red sequences, Proc. IEEE Workshop on Computer Vson beyond Vsble Spectrum, 3-12(1999). [11] Moon Y.-S, Zhang Tanxu, Zuo Zhengrong, Zuo Zhen, Detecton of sea surface small targets n nfrared mages based on multlevel flter and mnmum rsk bayes test, Internatonal Journal of Pattern Recognton and Artfcal Intellgence 14(7), 907~918 (2000). [12] WANG Chun-png, ZHU Yuan-chang, HUANG Yun-hua, Image Informaton-Based Trackng Algorthms Analyss, Fre Control & Command Control 25(1), 18-21(2000). Proc. of SPIE Vol I-7