Recognition and Classification of Human Behavior in Intelligent Surveillance Systems using Hidden Markov Model

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1 I.J. Image, Graphcs and Sgnal Processng, 2015, 12, Publshed Onlne ovember 2015 n MECS (hp:// DOI: /jgsp Recognon and Classfcaon of Human Behavor n Inellgen Survellance Sysems usng Adeleh Farzad Islamc Azad Unversy of Rash, Deparmen of Elecrcal Engneerng, Rash, Iran Emal: farzad.adeleh@yahoo.com Rahebeh arak Asl Unversy of Gulan, Deparmen of Elecrcal Engneerng, Rash, Iran Emal: narak@gulan.ac.r Absrac owadays, he human behavor analyss by compuer vson echnques has been an neresng ssue for researchers. Auomac recognon of acons n vdeo allows auomaon of many oherwse manually nensve asks such as vdeo survellance. Vdeo survellance sysem especally for elderly care and her behavor analyss has an mporan role o ake care of aged, mpaen or bedrdden persons. In hs paper, we propose a hgh accuracy human acon classfcaon and recognon mehod usng hdden Markov model classfer. In our approach, frs, we use sar skeleon feaure exracon mehod o exrac exremes of human body slhouee o produce feaure vecors as npus of hdden Markov model classfer. Then, hdden Markov model, whch s learned and used n our proposed survellance sysem, classfes he nvesgaed behavors and deecs abnormal acons wh hgh accuracy n comparson by oher abnormal deecon repored n prevous works. The accuracy abou 94% resuled from confuson marx approve he effcency of he proposed mehod when compared wh s counerpars for abnormal acon deecon. Index Terms Vdeo survellance, human acon recognon, sar skeleon mehod, feaure exracon, hdden Markov model. I. ITRODUCTIO Human acon recognon and classfcaon mehods have many dfferen applcaons useful n human lfe. Vdeo survellance s one of s aracve ulzaon whch s appled n nellgen supervson sysems n banks, parkng los and smar buldngs [1, 2]. Ineracon beween human and machne o order and communcaon s anoher mporan ssue, whch s done by varous echnques such as speech recognon [3] and hand gesure classfcaon [4]. The processng of vdeo frames come from secury cameras wh he am of conrollng and recognzng abnormal behavors creae an auomac care monorng sysem as a human acon recognzer. On he oher sde, he number of he elderly and he sck who lve alone and need o be checked by connuous monorng are ncreasng hus nellgen sysems are useful and necessary for elderly permanen monorng. Several facors are val n he effcency of an acon recognon sysem such as, deecon me, background of he locaon, he abnormal condons and he number of people n he neresed envronmen. The sgnfcance of each facor n he objec of sudy and he ype of acon or behavor denfy he ype of recognon and classfcaon. For nsance, n parly behavors jus he op par of body s used o hand gesure recognon [5]. Human behavor analyss from a capured vdeo requres a pre-processng sep ncludng foreground and background deecon, and rackng ndvdual n consecuve frames. The ohers major seps are feaure exracon, a suable classfer or model selecon and fnally he process of classfcaon, denfcaon and auhencaon based on exraced feaures. The frs sep for deecon of an objec behavor s denfyng he movemen of an objec n he mage and s segmenaon. The mos famous sraegy for movng objec deecon s background subracon [6]. A smple approach of background subracon s acheved by comparng each frame of he vdeo wh sac background. As we sad, afer pre-processng sep, an auomac recognon sysem ncludes wo fundamenal sages: frs sage s exracng feaures of he npu frame and he second sage s acons classfcaon [7]. One of he mos mporan seps n behavor analyss process s feaure exracon and creang a suable feaure vecor. Ths par of process wll generae he prmve daa for classfer. There are wde selecons of feaure exracon mehods n human acon recognon such as blob mehod [2], edge based mehod [8]. Furhermore, he exremes of human conour o s cenrod are one of he concepual feaures exraced from sar skeleon mehod [9]. Low compuaonal complexy and low sensvy o reszng are from he advanages of sar-skeleon mehod. In recognon sysem, a sequence of mages nroduces he acon, and ndependen of he feaure exracon mehod, he sysem produces a feaure vecor and convers o a Copyrgh 2015 MECS I.J. Image, Graphcs and Sgnal Processng, 2015, 12, 30-38

2 32 Recognon and Classfcaon of Human Behavor n Inellgen Survellance Sysems usng symbol whch s deecable by a classfcaon mehod [10]. Human acon classfcaon presened by dfferen sraeges n he prevous sudes follow feaure exracon sep. K-neares neghborhood (K) s a smple and useful classfer wh hgh compably o ake percepon and whou needng o creae hypohess on daa [11]. A drawback of K classfcaon s hgh compuaonal mng n learnng procedure. A good selecon of k value s anoher problem whch has o be se by dfferen smulaons. In Ref. [12], K-Means algorhm exracs feaures and K classfes dfferen acons. Suppor vecor machne (SVM) s anoher mehod of classfcaon [13] ha has ndcaed well performance n recen years n comparson wh he old mehods. Alhough SVM generally s used n wo class problems, by usng he sraegy of one-versus-one and one-versus-all case could solve mul class problems [14]. Hdden Markov model (HMM) presened n [15] s a hgh precson wh exra compung load classfcaon. In hs paper we use HMM as a hgh accuracy classfcaon mehod n our proposed survellance sysem. The remander of he paper s as follows: secon 2 overvews he relaed work. Secon 3 s a bref revew on hdden Markov model. Secon 4 descrbed he prncpal of our proposed survellance sysem. The smulaon resuls and comparson s presened n secon 5 and fnally, he paper s concluded n secon 6. II. RELATED WORK Many dfferen approaches for acon recognon have been proposed over he pas wo decades [16]. These researches have dfferen applcaons accordng o behavors varees. Sensors and cameras are wdely used for survellance applcaons. In some researches, he acceleraon obaned from sensors s used for human acon recognon, such as elderly people care n smar homes by sensors [17, 18]. The man dsadvanages of acceleraon based mehods are a person mus wear a parcular sensor or devce or place n a parcular place. The oher mehod s vdeo survellance, n hs mehod one or mul-camera s used n dfferen locaons for human behavor recognon. Ths ype of supervson has been used for human dfferen behavors recognon such as care behavor for elderly people and abnormal or crmnal behavors n ndoors or oudoors. Ref. [19] presens a parcle vdeo-based abnormal behavor deecon mehod and hdden Markov model s used for small groups of abnormal behavor deecon. An auomaed vdeo survellance for crme scene deecon usng sascal characerscs s presened n [20]. If he scene shows some pecular suaon such as purse snachng, kd nappng and fghng on he sree, he survellance sysem recognze he suaon and auomacally repor o agency. Anoher applcaon of vdeo survellance s elderly people behavor analyzng n emergency. In Ref. [21] he recognon of abnormal human acves such as fallng, ches pan and fanng, vomng, and headache s suded. The proposed sysem model presens a novel combnaon of R ransform and prncpal componen analyss (PCA) for abnormal acvy recognon. Hdden Markov model (HMM) s appled on exraced feaures for ranng and acvy recognon. Ref. [22] presened a mehod for human fall deecons based on combnaon of egenspace echnque and negraed me moon mages (ITMI). Egenspace echnque s appled o ITMI for exracng egen moon. On he oher hand, mul class SVM classfes and deermnes a fall even. Ref. [23] proposed a mehod o deec falls based on a combnaon of moon hsory and human shape varaon. Ref. [24] presens a HMM classfer for behavor undersandng from vdeo sreams n a nursng cener. To exrac an acvy from vdeo sream, s necessary o deec he foreground objecs and exrac mage feaures. Based on he exraced foreground pxel, a posure s represened by a par of hsogram projecon n boh horzonal and vercal. The moon compued from he moon hsory map (MHS) s also used as he feaures n deermnng he acvy and a duraon-lke HMM s adoped for acvy feaure exracon. Ref. [25] presens a novel mehod o deec varous posure-based evens n a ypcal elderly monorng applcaon n a home survellance scenaro. Combnaon of bes-f approxmaed ellpse around he human body, horzonal and vercal veloces of movemen and emporal changes of cenrod pon, would provde a useful cue for deecon of dfferen behavors. Exraced feaure vecors are fnally fed o a fuzzy mulclass suppor vecor machne for precse classfcaon of moons and he deermnaon of a fall even. In hs paper, he classfcaon and recognon human abnormal behavors are our focus. For hs purpose, exremes have been denfed wh suffcen accuracy by feaure exracng sep accordng o cener of gravy and poson of he body, and fnal feaure vecor s produced. Then HMM classfes accordng o exraced feaures and a he end abnormal behavors are deeced. III. A BRIEF REVIEW O HIDDE MARKOV MODEL Hdden Markov model s a powerful model for recognon random of evens and dynamc processes [15]. Tranng s one of he mos mporan ably of HMM. For ranng process, we apply a se of sequenal daa o HMM and esmae s prmary parameers. In hs paper, we use dscree HMM for classfcaon and recognon human behavor. A dscree HMM consss of a number of saes each of whch s assgned a probably of ranson from one sae o anoher sae and when he sysem s n a sae n parcular me such as s shown by q, (=1,2,...). Wh me ransons, saes occur sochascally. Lke Markov models, saes a any me depend only on he prevous sae or he sae a he precedng me. In a dscree HMM one symbol s yelded from one of he HMM saes accordng o he probables assgned o he saes. HMM saes are no drecly vsble, and can be observed only hrough a sequence of observed symbols [26]. To descrbe a dscree HMM, he followng noaons are Copyrgh 2015 MECS I.J. Image, Graphcs and Sgnal Processng, 2015, 12, 30-38

3 Recognon and Classfcaon of Human Behavor n Inellgen Survellance Sysems usng 33 defned [15]: = number of saes n he mode V= {v 1, v 2,...,v M }: se of possble oupu symbols. M = number of observaon symbols. Q = {q 1, q 2,...,q }: se of saes We dsplay sae ranson probably marx by A = {a j } acheved accordng o equaon (1): a P[ q j q ] 1, j (1) j 1 Where a j s he probably of ranson from sae o sae j. We dsplay symbol oupu probably marx by B = {bj(k) } and acheved accordng o equaon (2): b ( k) P[ O v q j] 1 M (2) j k Where O = (o 1,o 2,...,o T ) s he sequence of observaons, O T s he oupu a me and all he observaons dsplayed by T. Inal sae probably marx shown by π = {π } and s acheved accordng o equaon (3): P[ q ] 1 (3) For each of he above parameers, he model s defned compleely f here s a value for above parameers. So a HMM lke λ can be shown by a se of hree marxes as equaon 4. ( AB,, ) (4) A. Recognon and Tranng Usng HMM To denfy observed symbol sequences, we conceve one HMM for each caegory. For a classfer of C caegores, we choose he bes maches of model wh he observaons from C HMMs λᵢ ={Aᵢ, Bᵢ, πᵢ} =1,...,C. accordngly for a sequence of unknown caegory, we calculae Pr(λᵢ O) for each HMM λ ᵢ and selec λ c, where * c argmax( P ( O)) (5) Gven he observaon sequence O = (o 1, o 2,..., o T ) and he HMM λ, accordng o he Bayes rule, we should salve how o evaluae Pr(λᵢ O), he probably ha he sequence was produced by HMM λ. Ths probably s calculaed by usng he forward algorhm [27]. The forward algorhm s defned as follows: P( o, o,..., o q, ) 1 2 T α () s called he forward varable and s calculaed recursvely as follow: r (6) 1 bj( o1) 1 (7) 1 j a aj bj o T ( ) [ ( ) ] ( ) 1 j 1 (8) P( O ) ( ) (9) We calculae he lkelhood of each HMM usng he above equaon and selec he mos lkely HMM as he recognon resul. For learnng sage, each HMM mus be raned so ha s mos smlar o produce he symbol paerns for s caegory. Tranng an HMM means opmzng he parameers (A, B, π) of he model o maxmze he probably of he observaon sequence Pr(λ O). The Baum-Welch algorhm s used for hese esmaons. We should defne a number of varables before Baum-Welch algorhm defnon: ( ) P( o, o,..., o q, ) (10) 1 2 T β () s called he backward varable and can also be solved nducvely n a manner smlar o ha used for he forward varable α (). T T ( ) 1 1 (11) T 1, T 2,...,1 ( ) a b ( o ) ( j) 1 j j 1 1 j (12) P( O ) b ( o ) ( ) (13) To deermne he opmal sequence of saes s requred o defne a varable named γ as follows: ( ) P( q o, ) P( O, q ) PO ( ) P( O, q ) 1 P( O, q ) Ths equaon can be summarzed as follows: () 1 ( ) ( ) j ( ) ( ) (14) (15) And fnally Baum-Welch algorhm can be defned as follows: Copyrgh 2015 MECS I.J. Image, Graphcs and Sgnal Processng, 2015, 12, 30-38

4 34 Recognon and Classfcaon of Human Behavor n Inellgen Survellance Sysems usng (, j) P( q, q j O, ) 1 ( ) a b ( o ) ( ) j j 1 1 PO ( ) (16) Usng hese equaons, HMM parameers λ can be mproved o. The re-esmaon equaons from λ = (A, B, π) o ( A, B, C) are: a () (17) j T 1 b ( k) j (, j) 1 T 1 1 T 1 () 1 ovk T 1 () () (18) (19) IV. THE PRICIPLE OF OUR PROPOSED SURVEILLACE SYSTEM Toally, our proposed survellance sysem ncludes several seps. Some of hese seps are pre-processng and ohers are man seps. Fg. 1 shows our proposed survellance sysem for human acon deecon procedures. As shown n fgure, a frs we use background subracon algorhm for npu daa o exrac slhouee of body by foreground and background deecon. Then, we exrac exremes by sar-skeleon mehod. For hs purpose, we calculae he cenrod of he conour and provde he dsances of each pon on he conour from he cenrod n a couner-clockwse. Wh hs procedure, we fnd he local maxma of he exernal pons from dsance sequences and analyze he dsance dagram of conour exremes for exracng mporan pons or exremes of human conour. For feaure vecor producon, we use polar coordnae sysem for descrpon of he exremes. We place he cener of he polar coordnae sysem on he cenrod of he conour and produce feaure vecor by he poson of he pons n each dvson. As shown n Fg. 2. Alhough Baum-Welch algorhm does no always fnd he global maxmum, fnd he local maxmum of Pr(O λ). Par 1 Inpu vdeo Foreground &background deecon Human body slhouee exracng Par2 Produce feaure vecor by exremy pons Fnd exremy pons Feaure exracon Classfcaon by HMM Fg.1. Human Acon Recognon Procedures Vdeo Fg.2. The Exremy Pons of Human Body Slhouee n Sar Skeleon Mehod We choose egh angle and hree lengh dvsons hus we have a feaure vecor wh he lengh of 24. Accordngly, he number of pons couned n angle and lengh dvsons produce fnal feaure vecor of body conour. Overall, we have a feaure vecor for each vdeo frame and a me sequence by converng hese feaure vecors o dscree symbols. As we know HMM s an effecve mehod o analyze hese sequences. We apply Leave-one-ou mehod for ranng sep, so n each operaon, one of he vdeo samples s chosen as es sample and ohers are used for ranng, every me es sample change and oher samples are used for ranng. Therefore, as we sad, n a feaure vecor he number of Copyrgh 2015 MECS I.J. Image, Graphcs and Sgnal Processng, 2015, 12, 30-38

5 Recognon and Classfcaon of Human Behavor n Inellgen Survellance Sysems usng 35 mporan exremes pons couned and saved. Then hs nformaon s used as he npu of ranng sage and HMM parameers are raned. To ge HMM we use Baum-Welch algorhm and behavoral classfcaon o adjus suable HMM parameers from feaure vecor. Afer ranng HMM parameers for each class and n each sage of Leave-one-ou mehod, we es new sample of behavors. For hs purpose, we program a funcon ha akes new vdeos as npus accordng o HMM parameers n ranng sage and compare he feaure exraced from hese vdeos frames wh every HMM of each class or acon and acheve a probably for he class. Fnally he sysem selec he class wh he bes maches o desred acon and hs class s labeled as he resul of classfcaon for hs acon. V. THE SIMULATIO RESULTS OF CLASSIFICATIO FOR BEHAVIORAL SURVEILLACE DETECTIO In hs paper, we focus on behavors ha are useful for elderly care. To examne our mehod, we collec a daase as shown n Fg. 3. We consder a se of acon ncludng fallng from he bed, fallng from he char, collapsng, sng and bendng by several persons. Ths collecon consss of fve dfferen acons. For every acon, we examne seven dfferen samples hus alogeher we use 35 dfferen vdeo sequences. As we sad our survellance sysem fnally selec a class wh he bes maches o desred acon and hs class s labeled as he resul. The smulaons carred ou on a compuer sysem wh Wndows7, X64, Core 5, 2.13 GHz, RAM 4 GB. Fg. 4 shows he resul of our sysem recognon accuracy for dfferen samples of each acon. As shown n Fg. 4, he sysem recognze acons 1, 4 and 5 compleely rue bu he sysem s msaken n recognon of acon 2 and 3, because hey are somehow smlar ogeher. Acon1 Acon2 Acon3 Acon4 Acon5 Fg.3. Our Daase for Carng Behavors Includng Fve Acons Copyrgh 2015 MECS I.J. Image, Graphcs and Sgnal Processng, 2015, 12, 30-38

6 36 Recognon and Classfcaon of Human Behavor n Inellgen Survellance Sysems usng Accuracy (x 100) Accuracy Acon1 Acon2 Acon3 Acon4 Acon Daa samples Fg.4. Our Proposed Sysem Recognon Accuracy for Dfferen Samples of Each Acon Table 1 shows he accuracy of our survellance sysem by a confuson marx. In addon, Fg. 5 exhbs he color-coded bar char of he correc and ncorrec deecon accuracy for each acon, whch s derved from confuson marx. The summaon of resuls shows 94% accuracy n correc acon deecon. The proposed mehod works as a survellance sysem. When he sequence npu daa s checked, f s feaures s smlar o abnormal behavor such as fallng from he bed, fallng from he char and collapsng wh a hgh percenage, hs acon s labeled as abnormal behavors and acve an alarm. To show he effcency of he proposed approach, we have compared our survellance mehod wh 94% accuracy o s counerpars [24, 25], whch are brefly nroduced n relaed work secon. Ref. [24] has been proposed based on duraon-lke HMM classfcaon approach and s es smul are smlar o ours. For abnormal deecon, he approach has repored 90% accuracy. Mul-class SVM [25] repored resul show benefs of 88.8% accuracy for abnormal behavor deecon. Table 1. The Confuson Marx of he Daase Acon1 Acon2 Acon3 Acon4 Acon5 Acon1 100 Acon Acon Acon4 100 Acon Acon1 Acon2 Acon Acon4 Acon Acons Fg.5. Correc and Incorrec Deecon Accuracy for Each Acon Copyrgh 2015 MECS I.J. Image, Graphcs and Sgnal Processng, 2015, 12, 30-38

7 Recognon and Classfcaon of Human Behavor n Inellgen Survellance Sysems usng 37 VI. COCLUSIOS In hs paper, we propose a hgh accuracy behavoral survellance sysem for elderly care. Abnormal behavor deecon n our proposed sysem works based on HMM classfcaon. In our sysem, afer pre-processng seps sar-skeleon mehod s used o exrac body feaures and exremes. Then, some suable feaure vecors are generaed n polar coordnae sysem. Fnally, hs nformaon apples o he npu of HMM classfer whch s able o deec and label each npu acon. The smulaon resuls show he effcency of our mehod o correc deecon of fve dfferen acons as well as abnormal deecon. The accuracy of our mehod n elderly care survellance s 94% whch shows mproved n comparson wh he prevous smlar works presened n Refs. [24] and [25] wh 90% and 88% accuracy. REFERECES [1] Teddy Ko, A Survey on Behavor Analyss n Vdeo Survellance for Homeland Secury Applcaons, 37h IEEE Appled Imagery Paern Recognon Workshop, PP. 1-8, Aprl [2] S. Mhare, S. 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8 38 Recognon and Classfcaon of Human Behavor n Inellgen Survellance Sysems usng [24] Pau-Choo Chung, Chn-De Lu, A Daly Behavor Enabled for Human Behavor Undersandng, Paern Recognon (Elsever), vol. 41, pp , May [25] Homa Forough, Mohamad Alshah, Hamdreza Pourreza, Maryam Shahnfar, Dsngushng Fall Acves usng Human Shape Characerscs, Technologcal Developmens n Educaon and Auomaon (Sprnger), PP , [26] Junj YAMATO, Jun OHYA, Kenchro ISHII, Recognzng Human Acon n Tme Sequenal Images usng, IEEE Compuer Socey Conference on Compuer Vson and Paern Recognon, [27] X.D. Huang, Y. Ark, and M.A. Jack. "Hdden Markov Modes for Speech Recognon". Edmgurgh Unv. Press, Auhors Profles Adeleh Farzad receved her B.S. degree n Elecronc Engneerng from Islamc Azad Unversy of Lahjan, Iran, n She s also acceped n M.S. degree n Elecronc Engneerng n Islamc Azad Unversy of Rash, Iran, n Her curren research neress nclude human behavor analyss and classfcaon n nellgen survellance sysem and abnormal acon deecon. Rahebeh arak Asl receved her B.S. and M.S. degrees n Elecronc Engneerng from he Unversy of Gulan, Rash, Iran, n 1995 and 2000, respecvely. Also, she receved Ph.D. degree n elecrcal engneerng from he Iran Unversy of Scence and Technology, Tehran, Iran, n From 1995 o 2002 she has worked n elecronc laboraores of he Deparmen of Elecrcal Engneerng n he Unversy of Gulan. Durng 2002 o 2006, she was wh desgn crcu research group n he Iran Unversy of Scence and Technology elecronc research cener (ERC) and CAD research group of Tehran Unversy. Snce 2006, she has been an Asssan Professor n Deparmen of Elecrcal Engneerng, Engneerng faculy of Gulan Unversy. Her curren research neress nclude relable and esable VLSI desgn, objec rackng and machne vson sysems. Copyrgh 2015 MECS I.J. Image, Graphcs and Sgnal Processng, 2015, 12, 30-38