Object Tracking Based on PISC Image and Template Matching

Transcription

1 ect Trackng Based on PISC Image and Template Matchng Bud Sugand Electrcal Engneerng Department Batam State Polytechnc Batam Indonesa Astract Ths paper proposed a method for oect trackng y usng a perpheral ncrement sgn correlaton mage and template matchng. Ths method can e used to evaluate sgn changes of the neghorhood rghtness. The method s constructed from only the trend of the rghtness changes n the neghorhood of the pxel under consderaton. Consequently the mage s hghly roust to rghtness changes over the sequence and the smlarty of the orgnal texture pattern can e detected even f there s a rghtness change. In order to track the nterest oect among the oects detected n the feld of vew of camera the template matchng method was appled. At the last stage we evaluated the proposed method y dentfyng the oects usng ther color and spatal feature. The expermental results showed that proposed method has dentfcaton rate more than 9%. Keywords oect trackng; PISC mage; template matchng; color feature; spatal feature I. INTRDUCTIN In recent years wth the latest technologcal advancements vsual survellance and securty system receve a great deal of nterest. Untl recently vdeo survellance and securty system was manly a concern for mltary or large-scale companes. However ncreasng crme rate especally n metropoltan ctes necesstates takng etter precautons n securtysenstve areas lke country orders arports or government offces. Even ndvduals are seekng for personalzed securty systems to montor ther houses or other valuale assets. The sole answer for ths ncreasng demand for personal and socetal securty s automaton. The road range of applcatons motvates the nterests of many researchers worldwde to develop and uld the roust and relale securty system. Recent developments of securty system are ntroduced y many researchers especally n the felds of detectng and trackng the movng oects. Varous methods for detectng and trackng of movng oects have een proposed n the past. Lu et al. [] proposed a ackground sutracton to detect movng regons n an mage y takng the dfference etween the current and the reference ackground mage n a pxel-y-pxel fashon. It s extremely senstve to change n dynamc scenes derved from lghtng and extraneous events etc. Lpton et al. [] proposed a frame dfference technque that use of the pxel-wse dfferences etween two or three successve frames n an mage to extract movng regons. Ths method s adaptve to dynamc envronments ut generally does a poor o of extractng all the relevant pxels e.g. there may e holes left nsde movng enttes. Meyer et al. [3] proposed an optcal flow method y computng the dsplacement vector feld to ntalze a contour ased trackng algorthm called actve rays for the extracton of artculated oects. The optcal flow method can e used to detect ndependently movng oects even n the presence of camera moton. However most flow computaton methods are computatonally complex and very senstve to nose. In another work Wren et al. [4] used statstcal texture propertes of the ackground oserved over extended perod of tme to construct a model of the ackground and use ths model to decde whch pxels n an nput mage do not fall nto the ackground class. The fundamental assumpton of the algorthm s that the ackground s statc n all respects: geometry reflectance and llumnaton. Also Davs el al. [5] had approach that ased upon mage moton only presumng that the ackground s statonary or at most slowly varyng ut that the person s movng. In these methods no detaled model of the ackground s requred. These methods are only approprate for the drect nterpretaton of moton; f person stops movng no sgnal remans to e processed. These methods also requre constant or slowly varyng geometry reflectance and llumnaton. To overcome those prolems we propose a new method for trackng of movng oects employng perpheral ncrement sgn correlaton (PISC) [6] mage as an mage matchng method wth roust performance hgh accuracy and hgh computatonal effcency. The PISC mage s constructed from only the trend of the rghtness changes n the neghorhood of the pxel under consderaton. The extracton method proposed n ths paper focuses on dscrmnatng etween the smlar ackground areas wthout eng affected y rghtness changes due to adverse condtons y utlzng the roust matchng performance of the ncrement sgn correlaton procedure. The proposed method s ased on applyng such a smlarty decson to all pxels n the scene. In order to track the nterest oect among the oects detected n the feld of vew of camera the template matchng method was appled. At the last stage we evaluated the proposed method y dentfyng the tracked oects usng ther color and spatal feature [7].

2 The rest of the paper s organzed as follows. In secton we gve a detal of our proposed method to detect and track the nterest oect. In secton 3 the expermental and dentfcaton result of the proposed method are dscussed. Fnally the conclusons and the future works are descred n secton f f f f f II. METHD We dvded our proposed method nto three stages; oect detecton oect trackng and oect dentfcaton. n each stage we apply our new method and evaluate the effectveness of our method. The frst stage s done to detect the movng oect emergng n the ackground y applyng the PISC mage. The second stage s performed to track the movng oect usng template matchng. As the last stage we dentfy the tracked oect y extractng ther color and spatal nformaton of the oect. In ths paper we use mean and standard devaton value as mage feature of the tracked oect. The detals of each technque are descred as follows. A. PISC mage The perpheral ncrement sgn takes a value of or accordng to whether the ncrement near the consdered pxel s postve or negatve. Ths s a logcal code representng the trend of rghtness change. The ncrement sgn s a code that represents the trend of the rghtness change n a certan drecton. The correlaton coeffcent over the whole mage s called the ncrement sgn correlaton. The PISC mage s used to detect the movng person ased on the trend of the rghtness changes n the neghorhood of the pxel under consderaton. The PISC mage consder the rghtness changes n the 6-neghorhood of the consdered pxel as shown n Fg.. In order to detect the movng person n the mage sequence the ackground mage s defned frst. = N In the ackground mage { } F = f perpheral ncrement sgn k ( ) s defned as ( ) 5 ( ) = = ( f f ) ( otherwse ) = M ( f f ) ( otherwse ) the () G = g n the mage tme sequence ' k ( ) ( k =...5) s smlarly defned. In two mages the extent of matchng etween the ncrements sgns n the 6 drectons at the correspondng poston s defned as the perpheral ncrement sgn correlaton B: For any oect mage { } f f f f f f f f 3 Fg.. Neghorhood for perpheral ncrement sgn 6 f f f f 5 4 B = c k ( ) () k= C k = k ( ) k ( ) ( k ( )) ( k ( )) (3) The value of B at each pxel s compared to some threshold and a decson s made whether t s smlar or non-smlar pxel. The PISC mage s defned as nary mage {I } that defned as follows I = 5 ( B B ) T (4) ( otherwse) where B T s threshold usually takes a value n the range from.5 to. Fg. shows an example of a PISC mages n dfferent threshold. Fg. 3 shows the comparson etween ackground sutracton and PISC mage. n that mage we can understand that ackground sutracton s very senstve to change n dynamc scenes derved from lghtng and extraneous events whle the PISC mage can reduce the nose ecause of those effects. In the next step we performed a morphologcal operaton [8] to reduce the mage noses n the emergng oect. The morphologcal operaton mplemented n ths research s dlaton followed y eroson. In dlaton each ackground pxel that s touchng an oect pxel s changed nto an oect pxel. Dlaton wll add pxels to the oundary of the oect and close solated ackground pxel. In eroson each oect pxel that s touchng a ackground pxel s changed nto a ackground pxel. Eroson wll remove solated foreground pxels. Morphologcal operaton elmnates ackground mage noses and flls small gaps nsde an oect. Ths property makes t well suted to our oectve snce we are nterested n generatng oect masks whch preserve the oect oundary.

3 A (a) rgnal mage (a) (k-) th frame () PISC mage wth threshold.5 (c) PISC mage wth threshold.8 Fg.. Example of PISC mage () k th frame Fg. 4. Template matchng process () Background sutracton (a) rgnal mage (c) PISC mage Fg. 3. Comparson etween ackground sutracton and PISC mage Connected component laelng s performed to lael each movng oect emergng n the ackground. The connected component laelng groups the pxels nto components ased on pxel connectvty (same ntensty or gray level) [9]. In ths paper connected component laelng s performed y comparng a pxel wth the pxels n the four neghors from top-left to ottom-rght and from ottom-rght to top-left. B. Template Matchng In ths paper we use template matchng technque to search the nterest movng person among the oects appear on the scene. The algorthm of the template matchng method s shown n Fg. 4. Frstly to reduce the processng tme the template mages are made only n the area of the extracted person of (k-) th frame. The template sze s 9 9. Then each template mage of (k-) th frame search the target person n the area of the extracted person of k th frame. The correlaton value etween template mage of (k-) th frame and k th frame s evaluated y usng the expresson n (3). The matchng template wll have hgh correlaton value whle the nonmatchng template wll have low correlaton value. The numers of the matchng template of (k-) th frame and k th frame are counted. The target person n k th frame s determned as a same person n (k-) th frame when the numers of matchng templates are hgher than the threshold value. C. Feature Extracton and ect Identfcaton ect dentfcaton s the last stage of our study. In ths paper the extracted features are dvded nto two types; color and spatal nformaton of the movng oects. The RGB color s used as color nformaton of the movng oects. To otan more color nformaton for dentfcaton we dvde the movng oect nto three parts; the head the upper and the lower part of the ody. However we only calculate the color nformaton of upper and lower part of the human ody. The frst color nformaton calculated s mean value of each human ody part as calculated y (5). The mean value s calculated for each color component of RGB space.

4 xmax ymax ( xy ) x= xmn y= ymn = (5) μ # where s numer of the movng oects and (x y) s the coordnate of pxels n movng oect. ( max max ) ( ) x y and x mn y mn are the maxmum and mnmum coordnates of movng oect f k (xy) denotes pxel value for each color component n RGB space of the current frame denotes the set of the coordnate n the nterest movng oect and # s the numer of pxels of movng oect respectvely. We can extract more useful color features y computng the standard devaton of each human ody part as shown n (6). SD xmax y max ( ( x y) μ x= x f ) mn y= ymn k = (6) # l l where μ s the mean value and SD s the standard devaton of each color component of the movng oect respectvely. The feature of oects extracted n the spatal doman s the poston of the tracked oect. The oundng ox as defned n (7) s used as spatal nformaton of movng oects. B mn = {( x mn y mn ) x y } (7) B = x y x y {( ) } max max max where B mn s the left-top corner coordnates and B max s the rght-ottom corner coordnates respectvely. After the extracted feature s otaned we then calculate the smlarty etween the tracked oect and the dentfed oect as expressed n (8). The oect wth hgh smlarty compared to certan threshold shows the smlar oect to the dentfed oect otherwse t wll dentfy as dfferent oect. l l l l S( F F ) = Mc( μf μ k ) Mc( SDf SD k ) (8). 5Mp( Bmn B mn ). 5Mp( Bmax Bmax ) where Mc and Mp are memershp functon for color and spatal nformaton. III. EXPERIMENTAL RESULTS We have done the experments y usng a camera n outdoor envronments under nosy ackground and real tme condton over 3 4 pxels mage. We used the template sze of 9 9 pxels. The template mage was made from the oect appear for the frst tme. We track the nterest movng oect from two and three movng oects appear n the ackground. The expermental results are shown n Fg. 5 - Fg. 7. The rectangle area on the oect shows the extracted movng oect. n each experment we apply the dentfcaton process usng color and spatal feature of the movng oect. The dentfcaton result s shown n tale. For the frst experment as shown n Fg. 5 two movng oects are movng n the dfferent drecton. At frst the man wearng the whte shrt enters the scene from the left sde. Ths person wll e tracked as the nterest movng oect. Then on the next frame the man wearng the lue shrt enters the scene from rght sde. They move n the dfferent drecton and overlap each other n the mddle of the scene. We successfully track the frst movng oect as the nterest movng oect as our assumptons. In the second experment as shown n Fg. 6 two movng oects are movng n the same drecton. At frst the man wearng the lue shrt enters the scene from the left sde. Ths person wll e tracked as nterested movng person. Then on the next frame the man wearng the whte shrt enters the scene from left sde also. They move n the same drecton and overlap each other. We successfully track the frst movng oect as the nterest movng oect. The last experment as shown n Fg. 7 we track the nterest movng oect among three movng oects appear n the ackground. We also successfully track the frst movng oect as nterested movng oect. IV. CNCLUSIN AND FUTURE WRKS Ths paper proposed a method for detectng and trackng the movng oect ased on PISC mage and template matchng for real tme applcaton and an dentfcaton method usng color and spatal feature. By usng our proposed method the satsfactory results are acheved. The trackng results have een mproved usng PISC mage compared to conventonal method where usng PISC mage the rghtness change n the ackground and the shadng due to movng person are completely removed whle only the movng oect was extracted. By usng our proposed template matchng we successfully track the nterest oect among the oects appear on the scene. ur dentfcaton method usng color and spatal feature of the oect could extract the movng oects on the successve frame wth dentfcaton rates more than 9%. However our proposed method stll has lmtatons such as to determne the nterest oect when the other oects come nto the scene n the same tme. ur algorthm could not determne whch oect to e the nterest oect. And also when the oect too small to e tracked our algorthm could not dentfy as same oect as the nterest oect. The oect smlarty s low to e dentfed. Therefore the dentfcaton result s not %. The mprovement of the methods s necessary to mprove the algorthm and speed up the processng tme. These are remanng for our future works. REFERENCES [] Y. LIU A. Hazho and X. Guangyou Movng oect detecton and trackng ased on ackground sutracton Proc. Socety of Photo- ptcal Instrumentaton Engneers (SPIE) vol pp [] A. J. Lpton H. Fuyosh and R. S. Patl Movng target classfcaton and trackng from real-tme vdeo Proc. IEEE Workshop Applcatons of Computer Vson pp

5 [3] D. Meyer J. Denzler and H. Nemann Model ased extracton of artculated oects n mage sequences for gat analyss Proc. IEEE Int. Conf. Image Processng pp [4] C. Wren A. Azeraan T. Darrell and A. Pentland Pfnder: Realtme trackng of the human ody IEEE Trans. on Pattern Anal. and Machne Intell. Vol. 9 No.7 pp [5] J. W. Davs and A. F. Bock. The representaton and recognton of acton usng temporal templates Proc. of Computer Vson and Pattern Recognton pp [6] Y. Satoh S. Kaneko and S. Igarash Roust ect Detecton and Segmentaton y Perpheral Increment Sgn Correlaton Image System and Computer n Japan Vol. J 84-D-II No. pp [7] F. Cheng and Y. Chen Real tme multple oects trackng and dentfcaton ased on dscrete wavelet transform Journal of the pattern Recognton Socety Vol. 39 pp [8] E. Strnga Morphologcal change detecton algorthms for survellance applcatons Proc. Brtsh Machne Vson Conf. pp [9] R. C. Gonzalez and R. E. Woods Dgtal Image Processng Addson- Wesley Longman Pulshng Co. Inc. Boston MA.. TABLE I. IDENTIFICATIN RESULTS Experment Identfcaton rates [%] Fg. 5. Trackng the target from two movng persons move n the dfferent drecton Fg. 6. Trackng the target from two movng persons move n the same drecton Fg. 7. Trackng the target oect from three movng person emergng n the scene