High-level Hierarchical Semantic Processing Framework for Smart Sensor Networks

Transcription

1 HSI 2008 Kakow, Poland, May 25-27, 2008 Hgh-level Heachcal Semanc Pocessng Famewok fo Sma Senso Newoks Dema Buckne, Membe, IEEE, Jamal Kasb Rosemae Velk, Membe, IEEE, and Wolfgang Hezne, Membe, IEEE Venna Unvesy of Technology, Venna, Ausa {buckne, Ausan Reseach Cenes, Venna, Ausa {jamal.kasb Absac Ths pape pesens he famewok of a novel appoach o combne mul-modal senso nfomaon fom audo and vdeo modales o gan valuable supplemenay nfomaon compaed o adonal vdeo-based obsevaon sysems o even jus CCTV sysems. A heachcal, mulmodal senso pocessng achecue fo obsevaon and suvellance sysems s poposed. I ecognzes a se of pedefned behavo and leans abou usual behavo. Devaons fom nomaly ae epoed n a way undesandable even fo saff whou specal anng. The pocessng achecue ncludng he physcal senso nodes s called SENSE (sma embedded newok of sensng enes [1, 4]. Keywods senso newoks, senso fuson, semanc symbols, daa mnng, heachcal model I I. INTRODUCTION N cuen mes, obsevaon sysems fo publc spaces become moe wdespead. They ae a vsble eacon fo he publc on heas lke eosm and cme. Ths pape descbes he conceps of he semanc pocessng layes n a newok of SENSE nodes [1], [4]. The goal of hese layes s o lean he "nomaly" n he envonmen of a SENSE newok, n ode o deec unusual behavo, suaons, o evens and o nfom he cusome n such cases [5]. SENSE consss of a newok of communcang senso nodes, each equpped wh a camea and a mcophone aay. Those senso modales obseve he envonmen and delve a seam of socalled low-level symbols (LLS, e.g. movng objecs, o sounds. In he easonng un he LLSs ae pocessed n ode o nfom he peson n chage n case of above menoned evens occu. The fs applcaon aea of SENSE wll be an apo, heefoe all alams and ohe consdeaons ae akng no accoun he needs of he apo saff. The goal wll be acheved n seveal seps. Fs, he nfomaon eceved fom he senso laye,.e. he unmodal (audo and vdeo symbols, s pe-pocessed, whch ncludes deleon of spuous objecs and yng o ack symbols by coelang he eceved senso messages (snapshos a dsnc pons n me ove me. Second, he pe-pocessed un-modal symbols ae fused, whch esuls n negaed hgh-level symbols (HLS chaacezed by he combned un-modal popees. These symbols ae npu o he semanc symbol leanng pocess, whch deves he models fo ypcal behavo and popees of he dffeen objecs caegoes pesons, luggage, ec. These models descbe nomal behavo wh dsbuons of popees lke speed and decon wh espec of locaon. As he pahs whch symbols ake hough he (vsual sensng aea of a SENSE node ae an mpoan aspec of behavo, ajecoes ae deved fom he semanc symbol models. All of he mehods used n he pacula layes ae wdely used n e.g. obsevaon sysems and many ohe applcaons, bu o ou knowledge no ohe sysem uses a combnaon heeof n ode o le he messages of he sysem eally look sma and meanngful o he use. Ths pape s sucued as follows: he nex chape oulnes he sysem achecue, whle chape 3 descbes he ndvdual layes n moe deal. Chape 4 dscusses ackng of low-level symbols, and chape 5 conans concluson and oulook. II. ARCHITECTURE OVERVIEW An 8-e daa pocessng achecue s used, n whch he lowe levels wll be esponsble fo a sable and compehensve wold epesenaon o be evaluaed n he hghe levels (Fg. 1. A fs, he modaly symbols wll be checked egadng he plausbly he audo symbols wh espec o poson, he vdeo symbols wh espec o poson, and sze. In he second pocessng laye (n hs pape, we wll use e and laye as synonyms, symbol ackng wll ake place. Hee, symbols whch pass he fs (spaal check wll be checked egadng he empoal behavo usng a Makov Chan Mone Calo based pacle fleng appoach. The oupu of hs e s a sable and compehensve wold epesenaon ncludng un-modal symbols. Te 3 s he senso fuson e, n whch he un-modal symbols ae fused o fom mul-modal symbols. Laye 4 s he paamee nfeence machne, n whch pobablsc model(s fo symbols paamees and evens ae opmzed. The esuls of hs e ae models of hghlevel symbols and feaues ha descbe behavo. In e 5, he sysem leans abou ajecoes of symbols. Typcal /08/$ IEEE

2 pahs ough he vew of he senso node ae soed. The 6 h laye has he ask of managng he communcaon o ohe nodes and esablshng a global wold vew. The ajecoes ae also used n hs laye o fnd ou f a ajecoy of one node can be polonged ove a neghbong node. In e 7, he ecognon of unusual behavo and evens occus wh wo appoaches. One pa compaes cuen symbols wh he leaned models and ajecoes. Theefoe, hs sub-laye calculaes pobables fo he exsence of symbols wh espec o he poson, velocy, decon, and pobables fo ajecoes of symbols. I also calculaes pobables fo he duaon of say of symbols n aeas, pobables fo he movemen along ajecoes, also acoss nodes (global map, scenao ecognon. Symbols wh such pobables below defned hesholds ase "unusual behavo" alams. The second pa of e 7 s concened wh he ecognon of pe-defned scenaos and he ceaon of alams n case pe-defned condons ae me. Fnally, laye 8 s esponsble fo communcaon o he use. I geneaes alam o saus messages and fles hem f pacula condons would be announced oo ofen o he same even s ecognzed by boh mehods n laye 7. Neghbou node Hgh-level layes semanc pocessng Low-level laye Fg. 1. Semanc Pocessng Laye Sofwae Achecue The vsual feaue exacon (laye 0 pocesses fame by fame fom he camea n 2D camea coodnaes. Fs esuls fom he es vdeos show ha vsual deecon can delve sgnfcanly changng daa fom one fame o he nex. In case of unfounae condons fo he camea (many pesons, hey exchange posons n he cowd, bad lgh condons, ec, deeced symbols can change he label fom peson o objec o peson-goup and back (fo he same physcal objec. The sze of deeced symbols can change fom small elemens lke bags o lage goups of pesons coveng ens of squae mees and ncludng pevously deeced sngle pesons and ohe objecs. Consequenly, he hghe levels have o be pepaed o wok wh mpefecly deeced symbols. III. DESCRIPTION OF TIERS A. Low-level feaue exacon Descpon Ths laye s he pocessng un povdng he semanc pocessng laye wh un-modal daa seams descbng wha s obseved by he sensos of a SENSE node a me (.e. n he envonmen whee he node s embedded. The audo and vdeo low-level symbols (LLS epesen defned pmves (Vdeo: Peson, Peson Flow, Luggage, ec. Audo: Seps, Gun shoong, ec.. Funconaly The pacula funconaly of hs laye s no whn he scope of hs pape. We jus wan o pon ou ha he audo and vdeo daa mus be povded synchonzed,.e. me-samped wh efeence o he same clock. Inefaces Conssng of wo componens, one fo he audo and he ohe fo he vdeo modaly, hs laye povdes wo nefaces: Though he Audo Ineface, he audo symbols ae sen o laye 1. An audo LLS consss of a label, descbng he ype of he deeced audo symbols, he decon of aval, he loudness, ec. Though he Vdeo Ineface, he vdeo symbols ae popagaed. A vdeo LLS consss of a label, descbng he ype of he deeced vdeo symbols, he poson n pxel, he sze, he velocy, ec. B. Pe-pocessng ncludng plausbly checks Descpon Dung vsual feaue exacon, a se of emplaes s mached wh he cuen fame. The emplaes ae scaled n ode o fnd objecs of vaous szes. In ode o fle unealsc pmves fom he daa seam, a fs we nend o lean abou aveage sze of pmve symbols dependng on he ype and poson n camea coodnaes. The second plausbly check s done on boundng boxes. The boundng boxes of symbols ae aken o deemne whehe some peson o objec s blend no a lage objec. In hs case he coun of smalle and lage symbols decdes whch knd of symbol s mos pobable and wll be used fo fuhe pocessng. Smla o he sze of symbols, also he aveage speed wll be leaned by he senso. Ths nfomaon wll be used fo symbol ackng, oo. We assume he exsence of pons n mes, whee no pesons ae n he sensve aea of he

3 node. All nenal scenaos and symbols wll be ese a hese momens. When new LLSs appea, hey wll be acked ove me wh espec o he poson, sze, and speed. Funconaly Fo he aveage sze of symbols, a Gaussan o mxue of Gaussans model wll be ulzed. One aveage sze model wll be used fo pxel cluses, so ha he 640x480 camea pxels anslae o 40x30 pxel cluses, each 16x16 pxels lage. Each pxel cluse has models fo each ype of symbol. The models may need dffeen paamees dependen on he numbe of pesons,. e. may un ou ha people behave dffeenly n goups han hey do alone o n pas. Due o he fac ha symbols can change he ype fom fame o fame would no be a good soluon o delee all mpobable symbols, heefoe hey ae jus maked. Nex, we wll use fuzzy logc o fnd ou symbols ha do no change much fom fame o fame and so hem ou as beng able o ack. Thd, all ohe symbols ha may appea, dsappea, change he sze, ec.: o fnd ou f hey ovelap n he fame and ae no ecognzed as befoe because of he ovelap we ake he boundng boxes and es f a smalle objec blended no a lage o f a lage objec spl no smalle ones. If so, hese symbols may be coec deecons, bu we canno assgn speed, and we do no know f he deecon as small o bg symbol s domnan ove me. So, wh he me, he mely symbol coun fo lage o small symbol wll deemne how hs symbol s handed ove o he nex laye. Fnally, we wll compue he speed of symbols. In hs pocess, s necessay o evaluae moe han he mmedae pas fame. Rescons on he compuaonal powe wll show he possbles n hs espec. Afe all plausbly checks, a voe wll decde f a symbol s handed ove o he nex laye. C. Tackng Descpon Ths laye uses pacle fle echnques o ack he pepocessed symbols. The basc assumpons fo he algohm ae pesened n deal n he nex chape. Tadonally, mulple objecs (n he aea of pacle fles, objecs ae acked, no symbols, heefoe hs em s used hee ae acked by mulple sngle-objecackng fles. Whle usng ndependen fles s compuaonally acable, he esul s pone o fequen falues. Each pacle fle samples n a small space, and he esulng jon fle s complexy s lnea n he numbe of ages n. Howeve, n cases whee ages do neac, as n many of ou scenaos, sngle pacle fles ae suscepble o falues exacly when neacons occu. In a ypcal falue mode, seveal ackes wll sa ackng he sngle age wh he hghes lkelhood scoe. Funconaly A pacle fle specfcally desgned fo ackng neacng objecs [2] s used o ack he pe-pocessed symbols. The appoach fo addessng acke falues esulng fom neacons s o noduce a moon model based on Makov andom felds (MRFs [11]. D. Senso fuson Descpon Ths e ges as npu he sable un-modal symbols. Is ask s o fuse audo and vdeo symbols. One possbly s o use faco analyss [10, 12] o deemne he coelaon beween audo and vdeo LLSs. The oupu of hs e wll be a symbolc epesenaon of he eal wold n fom of a collecon of mul-modal symbols [13]. Funconaly Fuson of he audo and he vdeo daa s a ask ha can be done usng he coelaon beween he povded daa seams. Based on he me coelaon of LLS, feaues ha can be aken no accoun fo hs pupose ae: loudness, decon of avals, powe specum, sze of he vdeo symbol n pxels, and poson. E. Paamee Infeence Descpon Ths laye wll pocess he ncomng symbols of fused LLSs and adap he paamees of he used pobablsc model(s o f he daa. The daa ae defned as he se of all he nsances of semanc symbols. Funconaly Geng any symbol fom he senso fuson laye, he ask of hs laye s o nfe he paamees of he undelyng pobablsc model (Mxue of Gaussans, hsogams, o mxue of faco analyzes [3]. Usng an onlne veson of he Expecaon Maxmzaon (EM algohm o fnd he paamees of he model(s, we can focus on he vaan whee he sysem leans he behavo only of he ecen pas (me wndow, by usng onlne aveage movng. We also can assume ha he daa fs a sac model and heefoe we can use he "gaden descen vaan". The use of non-paamec mehods lke hsogams (n pacula, k neaes neghbos can be aken no accoun. Geneally, we ae usng onlne cluseng mehods lke descbed n [8], [9], [10], [11], [12]. F. Tajecoes Descpon Tajecoes n a node wll be deved hough he use of a leaned anson max conssng of ansons beween model cluses. Ths could be done by usng a local seach fo he mos lkely sequence. Each HLS (model mus heefoe keep a ls of all local ajecoes o whch he symbol s belongng and a he me, n whch an nsance s beng obseved and belongng o ha symbol, he suable ajecoy (whch should be acve fo he nsance mus be seleced. The node mus acvae all he local ajecoes whch ae possble a me fo he obseved nsance. Addonally, all he neghbong nodes, o whch he node has some coelaons, mus also acvae he suable ajecoes. Due o he fac ha one HLS could belong o moe han one ajecoy (afe leanng, he possble ajecoy wll no be necessaly unque. Bu addng he eal me nfomaon,.e. he nsance a me, whch s belongng o jus one HLS, and whch s n un belongng o one o moe ajecoes, he

4 local and global ajecoy s unambguously denfed. Funconaly The dynamcs of he deeced objecs n he envonmen of he node ae descbed by buldng he local ajecoes map of a node. The map buldng pocess makes use of he abues of he HLS; especally, uses he velocy abues mean and vaance n he coesponden poson. Each of he nvolved nodes has o lean he global possble ajecoes (ncludng he pobably of havng he ajecoy acve, he densy - hgh, mddle, low - he decon, and he velocy. Then each node keeps a lookup able o a max whee all he possble ajecoes ae egseed. A smple swch fom one local o global pah o anohe one mus cause an alam (because s unusual ha people ake hs pah. Ths max of all he possble ajecoes wll be bul usng he local anson maces and he wegh maces (coelaons beween he HLS n he dffeen nodes. G. Ine-Node Communcaon Descpon Ine-node communcaon s based on he Loopy-Belef Popagaon (LBP algohm [6, 7]. LBP wll be used o fom collecons of neghbong nodes and o map he posons whn one node s vew no anohe one s vew. Ths nfomaon can fnally be used o e.g. soe he ajecoy of pesons ove mulple nodes, o o fnd ou f somebody es o escape obsevaon. Funconaly Ths module wll be a he hea of he nfomaon exchange whn he SENSE sysem. Ths laye sends and eceves messages fom and o ohe nodes n he SENSE sysem. Those nodes mus be eachable by he nodes (.e. "neghbos" wh sensng aeas whch ovelap wh ha of he especve node. The nfomaon of neghbohood s soed n a max and s heefoe a dynamc paamee ha can change ove me. Anohe ask of hs laye wll be o updae he compables and heefoe he weghs beween HLSs. Inefaces Laye 6 (Ine-Node communcaon Laye 7 (Alam Geneao: We assume ha he sub laye nesenso communcaon povdes he sub laye alam geneao wh he global coelaons ha concens he map (ajecoes, he hgh-level symbols, he global ackng of pesons, and he coesponden abues. Ths affecs he node-o-node neface. Laye 6 (Ine-Node communcaon Neghbos: Fo leanng he global sucue hough he hgh-level symbols, only wo vaables wll be exchanged: he belef of he node gven he evdence a me and also he belef of he node whou he evdence a me. (The evdence s he senso eadng. The exchange of he HLS ncludng s paamees (abues, deeced nsances a me, local ajecoes, and global ajecoes should also be done. I wll be nvesgaed whehe ohe nfomaon can also be exchanged. H. Alam Geneao Descpon Ths e seves o deec pedefned alams and fuhe unusual behavo. I geneaes alams n case of vey unlkely symbols (as a esul of he above menoned ackng and pobably esmang asks. A second paly ndependen pa wll be he deecon of pedefned scenaos: The nfomaon of pesons movng, meeng, havng luggage, ec. wll be compaed wh emplae scenaos. If possble, hese scenaos (e.g. one peson comng wh luggage, doppng luggage, leavng wll be used o mono he behavo of pesons and fo alamng. The majo dffeence beween pedefned alams and ecognzed unusual behavo s ha he fs can be assocaed wh a pedefned, human-eadable ex, lke "sceam nose" o "peson dopped luggage". In conas, wh unusual behavo only he nvolved symbols(s can be denfed (n he use neface, whou fuhe explanaoy ex assocaed (besdes poenal namng of he feaues deeced as beng ou of nomal. Funconaly 1: (Pedefned scenao ecognon Ths mehod akes he symbolc epesenaon on he level of he modaly elaed symbols as npu. I uses a ule-base o combne hese symbols n a heachcal way o ceae symbols wh hghe (moe absac semanc meanng ou of symbols wh lowe semanc level. Pedefned alams ae no flexble and hus canno adap o new suaons. Whaeve s ecognzed has o be bul no he sysem befoe becomes opeave as opposed o ecognzng unusual behavou, whch leans dung opeaon. The advanage of ecognzng pedefned alams s he ably o povde a human use wh a semanc descpon of he ype of alam ha he sysem has deeced (e.g. Unaended Luggage nsead of Unusual Behavo, also fo complex scenaos. The pedefned alam scenaos ha he sysem a he apo shall deec ae: Unaended luggage Loeng peson Ca n pakng aea has exceeded maxmum pakng me Sceamng peson Gunfe Beakng glass Whle he pedefned alams Sceamng peson, Gunfe and Beakng glass meely ely on nfomaon avalable fom low-level symbols of he audo modaly and wll be handed hough o he Use Nofcaon Fle, he alams Unaended luggage and Loeng peson eque a symbolc pocessng of dffeen nfomaon souces as descbed n [10]. Unaended luggage To deec unaended luggage, he sysem has o deec peson objecs and luggage objecs. When hese wo objecs can be elably deeced, he sysem can eason abou he assocaons beween peson and luggage symbols. The fs scenao s a luggage symbol, whch canno be assgned o a peson elably. If he sysem fals

5 o assgn he luggage fo a cean peod, assumes he luggage o be unaended. Ths scenao s a successful connecon beween a peson and a pece of luggage. In he second scenao, he sysem has o ack boh peson and luggage and deec f he peson has moved away fom he luggage fo a longe peod. I s expeced ha he fs scenao yelds moe false alams, snce eles only on he successful vsual deecon of luggage. The second scenao, howeve, s expeced o be moe elable and fuhemoe possbly allows denfcaon of he peson who has lef he pece of luggage. Loeng peson Peson symbols ha can be successfully acked fo a longe peod can ase an alam fo loeng f hey eman n he same locaon fo an exended peod of me. The challenge hee s o pove ha loeng can be deeced, even f he peson shfs s poson beween he vewpo of dffeen cameas. Thus he sysem shall be able o deec a peson ha has been movng aound he aea fo e.g. a whole day. Whou ne-node communcaon, he sysem can be fooled by changng he poson beween dffeen camea posons. By handng ove denfed pesons beween nodes, hs should no be possble. Funconaly 2: Unusual behavo ecognon Pobably of exsence: Ths mehod calculaes he pobably of a symbol wh espec o s poson whn he senso s vew, s decon of movemen, and s speed. The esul wll be Pobably Mounans ove he menoned paamees. Pobably of duaon: In addon o he pobably of he smple exsence of symbols, hs ask measues he duaon of exsence of symbols whn some ange. The ange can be composed of pas of he senso s vew up o he vew acoss seveal sensos. Pobably of movemen: The pobably of he movemen of a HLS whn he node s vew (e.g. s usual ha a peson comng fom one ajecoy moves o anohe one? and acoss nodes: Gven an nsance ha s obseved a me, he pobably ha hs nsance wll follow he same pah (o akes he same ajecoy as ohe nsances should be calculaed. Also a swch fom one ajecoy o anohe should be handled as an alam. I. Use Nofcaon Fle Descpon Ths e bulds he neface of he hgh-level senso pocessng. I delves alams o he use neface and can be asked abou he saus of a node o seveal nodes. I fles dencal alams, e.g. when he same lukng peson s epoed seveal mes fom he pedefned scenao ecognon, o a epoed unusual behavo can be mached wh a pedefned alam, only he lae wll be delveed. Addonally, he use can apply fleng ules o om o polong alams va he GUI. Funconaly A basc fleng mechansm fo avodng sendng he same alam seveal mes o sendng a pe-defned alam and an unusual behavo fo he same hng s appled. An addonal ule-base wh use pefeences s also consdeed. Noe: alhough by means of LBP a global vew wll be esablshed among he nodes and despe he fleng, canno be excluded ha he GUI wll eceve dencal alams fom dffeen nodes. IV. DETAILED DESCRIPTION OF TRACKING OF LLS We ae concened wh he poblem of ackng mulple neacng ages. Ou objecve s o oban a ecod of he ajecoes of ages ove me and o manan coec, unque denfcaon of each age. Tackng mulple dencal ages becomes challengng when he ages pass close o one anohe o mege as pesons do n a cowd. In ecen mes, an appoach ha eles on he use of a moon model ha s able o adequaely descbe age behavo houghou an neacon even was developed [2]. Ths appoach has a moon model ha eflecs he addonal complexy of he age behavo. The eason fo usng hs appoach les n he fac ha he numbe of symbols n he obsevaon model can change fom senso obsevaon o senso obsevaon. E.g. f seveal pesons ae gong hough a codo, he vsual feaue exacon algohms mgh deec a sasfacoy numbe of pesons n one mage and jus a goup of pesons n he consecuve one. In case of unlucky condons, he deecon can change ofen whn sho peods of me fo he same physcal objec. The mulple age ackng poblem can be expessed as a Bayesan fle. We ecusvely updae he poseo dsbuon P ( Z ove he jon sae of all n ages { 1.. n} gven all obsevaons Z = Z1.. Z up o and ncludng me, accodng o: P ( Z = kp ( Z 1 ( Z 1 1 Z 1 The lkelhood P expesses he measuemen model, he pobably we obseved he measuemen Z gven he sae a me, whch s a model fo he modaly-elaed feaue exacon algohms. The moon model P ( 1 pedcs he sae a me gven he pevous sae 1. In all ha follows we wll assume ha he lkelhood P Z facos acoss ages as Z = n = 1 ( Z and ha he appeaances of ages ae condonally ndependen, whch may no be compleely ue n case of people, who belong ogehe, bu wll hold mos of he me beween all pesons n he cowd. If we assume he ages as beng ndependen, o nonneacng, hey can be acked wh sngle-age pacle

6 fles. In ohe wods, he moon model s facoed n a poduc of moon models fo ndvdual ages P ( =, 1 1. The ask s o appoxmae he poseo P ( Z ove each age s sae. In ohe wods, he pobably ha he cuen obsevaons ae made, because he obseved objecs behave n a pacula way. One vew on pacle fles s o see hem as mpoance fle fo hs poseo. Theefoe, we assume he poseo of he pevous me beng appoxmaed by a se of weghed pacles, 1 ( ( { } N Z 1, π 1. = 1 Then, fo he cuen me sep, we daw N samples (s fom a poposal dsbuon ~ q( ( = π ( s 1 ( 1 ( whch s a mxue of moon models, 1. Fnally, we wegh each sample by s lkelhood. The ( s ( s ( s N esulng se {, π = Z } s= 1 s a weghed appoxmaon fo he poseo ove he age s sae a me. The MRF-based appoach fo he moon model uses pa wse MRFs, whee he ψ (, j ae pa wse neacon poenals (along edges and E he space of edges beween objecs: ( 1 ( 1 ψ (, j P Each wo objecs shae a pacula poenal. Ths em can be ncopoaed no he Bayesan fle easly, bu now we appoxmae he jon sae of all ages, whch would esul n he necessy of dawng an ncedble hgh numbe of pacles o be able o fnd a good appoxmaon. Applyng he Mone Calo appoxmaon o he ognal Bayesan poseo, we ge ( ( π, P ( Z k Z 1 1 and ncopoang he MRF moon model, we oban ( ( P ( Z k Z ψ (, π j 1 1 Founaely, we see ha he neacon poenal s ndependen on eale saes, so can be eaed as addonal faco. Unfounaely, appoxmang hs em appoxmaes he jon poson of all ages, whch s no ou scope. Theefoe, we apply MCMC (Makov chan Mone Calo samplng, so ha he saonay dsbuon of he chan s exacly he age dsbuon, and we change only he sae of one age a a me by samplng decly fom he moon model of ha age / 1 / 1 /' ( / Q ( = Q(, = 1 δ ( j = N N j The accepance ao of hs samplng mehod s only / / / Z ψ (, j as = mn 1,. Z ψ (, j Ths mehod mnmzes he compuaonal effo n compason o jon pacle fles fo ackng of mulple objecs and also mnmzes he faul deecon ae compaed o a se of sngle-objec-ackng pacle fles. V. CONCLUSION AND OUTLOOK Ths pape pesens a heachcal pocessng achecue fo sma senso newoks. The nnovave aspec les n he sep-by-sep pocessng, hough whch fom he low-level symbols hee emeges nfomaon beng moe and moe meanngful o a human peson n chage. Expeced esuls ae descbed fo he deploymen n an apo envonmen. All layes of he heachcal pocessng famewok ae descbed n ode o undesand he dea behnd and he algohm fo ackng of mulple objecs s descbed n moe deal. Ieave ess and nevews wh he apo saff ae planned o gan measues fo evaluang he esuls. REFERENCES [1] (SENSE pojec webse [2] Z. Khan, T. Balch, and F. Dellae: An MCMC-based Pacle Fle fo Tackng Mulple Ineacng Tages, IEEE Tansacons on Paen Analyss and Machne Inellgence, 2006 [3] Bshop, C. M.: Neual Newoks fo Paen Recognon. New Yok NY.: Oxfod Unvesy Pess Inc., 1995 [4] G. Zucke (ne Pal, and L. Fangu: Sma Nodes fo Semanc Analyss of Vsual and Aual Daa, Poceedngs of he IEEE INDIN, p , 2007 [5] B. Sallans, D. Buckne, and G. Russ: Sascal Deecon of Alam Condons n Buldng Auomaon Sysems. In: Poceedngs of 2006 IEEE INDIN, S. 6, 2006 [6] C. Cck and A. Pfeffe: Loopy belef popagaon as a bass fo communcaon n senso newoks, In Poceedngs of Unceany n Afcal Inellgence (UAI, 2003 [7] J.S. Yedda, W.T. Feeman, and Y. Wess: Undesandng Belef Popagaon and Is Genealzaons, IJCAI 2001 [8] D.-S. Lee: Onlne adapve gaussan mxue leanng fo vdeo. applcaons, n ECCV 2004 Wokshop on Sascal fo Vdeo Pocessng [9] N. Vlasss, and A. Lkas: A Geedy EM Algohm fo Gaussan Mxue Leanng, Neual Pocessng Lees, Volume 15, Numbe 1, 2002 [10] Z. Ghahaman and M. J. Beal: Vaaonal Infeence fo Bayesan Mxues of Faco Analyses, Advances n Neual Infomaon Pocessng Sysems. 2000, vol. 12, MIT Pess [11] N. Ueda, R. Nakano, Z. Ghahaman and G. E. Hnon: SMEM Algohm fo Mxue Models, Neual Compuaon achve Volume 12 [12] Z. Ghahaman and G. E. Hnon: The EM Algohm fo Mxue of Faco Analyzes, Techncal Repo CRG-TR-96-1, Depamen of Compue Scence, Unvesy of Toono, [13] P. Lombad: A sudy on daa fuson echnques fo vsual modules, Techncal. epo, Unvesy of Pava, 2002 [14] W. Bugsalle: Inepeaon of Suaons n Buldngs, Dsseaon hess, Venna Unvesy of Technology, 2007 [15] R. Kndemann, and J. L. Snell: Makov Random Felds and The Applcaons, AMS Books Onlne, ISBN: ,