HEAD DETECTION AND TRACKING SYSTEM

Transcription

1 HEAD DETECTION AND TRACKING SYSTEM Akshay Prabhu 1, Nagacharan G Tamhankar 2,Ashutosh Tiwari 3, Rajsh N(Assistant Profssor) 4 1,2,3,4 Dpartmnt of Information Scinc and Enginring,Th National Institut of Enginring, Mysor. Abstract - HDTS (Had dtction and Tracking Systm) dtcts and tracks multipl popl in a ral tim vido stram in an fficint way. HDTS dtcts hads and facs in an outdoor and indoor nvironmnt and kps track of thm. HDTS uss Partial Had Contour mthod to dtct had and Fatur invariant mthods to dtct fac. Had is locatd at an xtrm position of th body and thr is noticabl diffrnc btwn th had and th shouldrs in th input imag. Fac can b dtctd by finding th distanc btwn ys, colour intnsity btwn ys and othr parts of th facs. HDTS is abl to r-dtct a prson vn if h/sh is invisibl for som tim. Popl can bcom invisibl if thy wall bhind pillars or walls. Th principl in dvloping this systm can b xtndd to solv othr problms such as human gstur rcognition and fac rcognition. Kywords: Fac dtction, Had dtction, tracking. I. INTRODUCTION Fac dtction is a computr tchnology that idntifis human facs in digital imags. It dtcts human facs which might thn b usd for rcognizing a particular fac. This tchnology is bing usd in a varity of applications nowadays. Fac dtction is usd in biomtrics, oftn as a part of a facial rcognition systm. It is also usd in vido survillanc, human computr intrfac and imag databas managmnt. It is widly acknowldgd that th fac rcognition hav playd an important rol in survillanc systm as it dosn t nd th objct s coopration.. Th tracking adoptd by HDTS continus vn if thr ar various dgrs of occlusion in th corrsponding frams. Fig.1 rprsnts th block diagram of ntir HDTS systm. It shows that HDTS taks a vido stram as an input and it snds that vido as argumnts to two moduls: dtct fac and dtct had. In dtct fac th fac prsnt in th vido will b dtctd. Similarly in dtct had, hads will b dtctd. Aftr dtcting facs and hads thos two will b mrgd in such a way that no ovrlap of facs and had happns and this will b trackd. Finally th vido with dtctd facs and hads will b playd. Dtct Fac Mrgd facs Input Vido Dtct Had Mrg and Track Dtctd Had and Fac Mrgd hads Play Vido All rights Rsrvd 537

2 Intrnational Journal of Modrn Trnds in Enginring and Rsarch (IJMTER) Volum 02, Issu 04, [April 2015] ISSN (Onlin): ; ISSN (Print): II. EXISTING SOLUTIONS In this sction w dscrib about th xisting solutions to two major problms in HDTS systm: Had dtction and Fac dtction. 2.1 Had Dtction Dtcting a human had is important in many applications in vryday lif such as occludd fac dtction and rcognition, human tracking and diagnostic in mdical systm. In th prsnt, vido survillanc systm is widly usd and rcords data into 2D imag. Howvr, most of currnt mthods prform ffctivly whn facial rgion is availabl. Thrfor, it is important to us an algorithm which is pos invariant and is capabl of dtcting hads fficintly in various dgrs of occlusion. HDTS aims to dtct human had from any viwpoint of had from a vido squnc. Shap contour matching algorithm [9] for Had dtction is applid to th input imag fram to dtct and locat had shap objcts basd on a probabilistic framwork. In th procss of had shap dtction, uppr half circl is usd as had shap tmplat for locating th potntial had candidats in imag. Latr rsarchrs showd that dtctd had, basd on th assumption that human had and body contours, hav an omga shap. Thr faturs which ar gray, skin colour and dg is usd to dfin human rgion and thn Ellips fitting tchniqu [11] is usd to dtrmin position of had. 2.2 Fac Dtction Fac dtction must dal with svral wll-known challngs. Thy ar usually prsnt in imags capturd in uncontrolld nvironmnts, such as survillanc vido systms whr lot of variations in position of prson, lighting conditions, movmnt of camra occurs. Thr ar many tchniqus to dtct a fac in a scn-asir and hardr ons. Hr is a list of th most common approachs in fac dtction: Finding facs by colour: Using typical skin colours to find fac sgmnts. Th disadvantag is it dosn t work with all kind of skin colour and is not vry robust undr varying lighting conditions. Facial xprssion: Popl can hav diffrnt facial xprssion lik laughing, crying, angry fac, closd ys tc. Facial faturs also vary gratly bcaus of diffrnt facial gsturs. III. PROPOSED SOLUTION 3.1 Had Dtction To ovrcom th limitations, HDTS uss Blob analysis mthod to dtct had. HDTS assums that th had which nds to b dtctd is a bar had without any occlusions i.., prson should not b waring any hats or any othr objcts that covr th had rgion and th prson is in an upright position. In this mthod first HDTS acquirs th imag from th vido thn uss background subtraction [10] mthod to rmov th background. Thn it prforms blob analysis basd on th shap of th had to dtrmin th possibl position of th had rgion. Fig. 2 blow shows th flow diagram for had All rights Rsrvd 538

3 Intrnational Journal of Modrn Trnds in Enginring and Rsarch (IJMTER) Volum 02, Issu 04, [April 2015] ISSN (Onlin): ; ISSN (Print): Subtract Is an Fig.2 Tr u F a ls 3.2 Fac Dtction In HDTS w us Viola Jons algorithm to dtct th fac. Th basic principl of th Viola-Jons algorithm is to scan a sub-window capabl of dtcting facs across a givn input imag. Th standard imag procssing approach would b to rscal th input imag to diffrnt sizs and thn run th fixd siz dtctor through ths imags. This approach turns out to b rathr tim consuming du to th calculation of th diffrnt siz imags. Contrary to th standard approach Viola-Jons rscal th dtctor instad of th input imag and run th dtctor many tims through th imag ach tim with a diffrnt siz. Fig. 3 shows th flow diagram for fac dtction. T r Fac u Mov If fig3 F a l s IV. APPLICATIONS It is commonly usd in applications such as scurity, survillanc, and against trrorist activitis. It can b also usd to stimat th quu lngth in rtail outlts. Hom scurity systms ar anothr fild whr this can b applid. In collgs, this can b usd to track th attndanc of studnts and lcturrs. In offics, this can b usd for automatd controlling of lights and fans on th prsnc of popl in a room. V. CONCLUSIONS AND FUTURE WORK HDTS in futur can b nhancd to dtct prson vn without a bar had i.., it can dtct and track a prson vn if h is waring a hat. HDTS in futur can also b usd as basis for othr systms such as facial rcognition systm. Hr HDTS had dtctd facs of popl so in futur it would b nough to just apply a facial rcognition algorithm to this. So using this addd fatur of facial rcognition All rights Rsrvd 539

4 Intrnational Journal of Modrn Trnds in Enginring and Rsarch (IJMTER) Volum 02, Issu 04, [April 2015] ISSN (Onlin): ; ISSN (Print): HDTS, in addition to tracking popl w can also gt dtails of thos popl. Anothr improvmnt could b rcognizing th gsturs prformd by th popl. Whil tracking popl w can apply a gstur rcognition algorithm to know what popl ar doing. And basd on what thy ar doing w can tak som actions if ncssary and also its accuracy can b improvisd for dynamic background vidos. This can b don by applying som dynamic background modlling and subtraction algorithms lik rtrival of thrshold signals, local dpndncy histogram, and covarianc basd mthods and fuzzy background subtraction mthod. VI. REFERENCES [1] A. Colmnarz, R. Lopz and T. Huang, 3D Modl-Basd Had Tracking, Visual Communication and Imag Procssing,San Jos, CA, [2] Burt, P. J. (May 1981). "Fast filtr transform for imag procssing". Computr Graphics and Imag Procssing 16: doi: / x(81) [3] Shan Lu, Gabril Tschpnakis and Dimitris N. Mtaxas, Blob analysis of Had and hands: A mthod for dcption dtction, univrsity of Arizona. [4] F. D la Torr, E. Martinz, M. E. Santamaria and J.A.Moran, Moving Objct Dtction and Tracking Systm: a Raltim Implmntation, Procdings of th Symposium on Signal and Imag Procssing GRHDTSI 97, Grnobl, [5] Frund, Yoav; Schapir, Robrt E. (1995). A Dcision-Thortic Gnralization of on-lin Larning and an Application to Boosting. CitSrX: [6] H. Yoon, D. Kim, S. Chi and Y. Cho, A Robust Human Had Dtction Mthod for Human Tracking, IEEE/RSJ Int. Conf. on Intllignt Robots and Systms, [7] I. Haritaoglu, D. Harwood and L.S. Davis, W4: Ral-Tim Survillanc of Popl and Thir Activitis, IEEE Trans. Pattrn Analysis and Machin Intllignc, 22(8),2000, pp [8] M. Chn, G. Ma and S. K, Multi-viw Human Had Dtction in Static Imag,MVA2005 IAPR Conf. On Machin Vision Applications, Japan,2005. [9] J. S. C. Yuk, KY. K. Wong, R. H. Y. Chung, F. Y. L.Chin and K. P. Chow, Ral Tim Multipl Had Shap Dtction and Tracking Systm with Dcntralizd trackrs, Proc. 6 th IEEE Int. Conf. on Intllignt Systm Dsign and Application (ISDA06), [10] Chung-Chng Chiu, Min-Yu Ku "Robust Background Subtraction Algorithm in Intllgnc Traffic Systm",Journal of Miho Institut of Tchnology. [11] Sandra Lach Arlinghaus, PHB Practical Handbook of Curv Fitting. CRC Prss, [12] Ol Hlvig Jnsn Implmnting th Viola-Jons Fac Dtction Algorithm, Kongns Lyngby 2008, IMM-M.Sc [13] Z. Zhou, A. Wagnr, H. Mobahi, J. Wright, Y. Ma, "Fac rcognition with contiguous occlusion using markov random filds," IEEE 12th Intrnational Confrnc on Computr Vision [14] Ol Hlvig Jnsn Implmnting th Viola-Jons Fac Dtction Algorithm, Kongns Lyngby 2008, IMM-M.Sc - All rights Rsrvd 540

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