An Approach for Crowd Density and Crowd Size Estimation

Transcription

1 JOURNAL OF SOFTWARE, VOL. 9, NO. 3, MARCH An Approach for Crowd Densty and Crowd Sze Estmaton Mng Jang 1,2, Jngcheng Huang 1,2, Xngq Wang 1,2, Jngfan Tang 1,2, Chunmng Wu 3 (1. Insttute of Software and Intellgent Technology, Hangzhou Danz Unversty, Hangzhou, , Chna) (2. Zhejang Provncal Engneerng Center on Meda Data Cloud Processng and Analyss, Hangzhou Danz Unversty, Hangzhou, , Chna) (3. College of Computer Scence and Technology, Zhejang Unversty, Hangzhou, , Chna) Emal: jmzju@163.com, zjcnxghjc@126.com, xqwang@163.com, tangjf@hdu.edu.cn, wuchunmng@zju.edu.cn Abstract Crowd estmaton s very mportant n ntellgent crowd montorng. In ths paper, a new approach of crowed estmaton s proposed. Ths method combnes the advantage of pxel statstcal feature and texture analyss, and reduces the mpact of perspectve dstorton by dvdng the regon of nterest. Moreover, we estmate the crowd sze (the range of numbers) for hgh and extremely hgh crowd. The experment results show that the proposed method has a relatvely hgh accuracy n the whole range. Index Terms Regon of nterest, Crowd estmaton, Crowd sze estmaton, Pxel statstcal, GLCM. I. INTRODUCTION Wth the development of rapd economc, large-scale human actvtes have become ncreasngly frequent, especally some massve entertanment events, sportng events, and large exhbtons. Crowd safety has become a crtcal ssue, causng the attenton of the securty sector. As we know, the terrorst attacks n large groups can result n greater lethalty and socal mpact, large-scale group actvtes are now mportant goals of terrorst attacks. So t s very meanngful to estmate crowd. In order to analyss group events better, we need to estmate the sze of the crowd rather than the crowd. Tradtonal vsual survellance applcatons are based on CCTV (Closed Crcut Televson) analog systems. Although such system enhances the human vsual ablty through the hardware devce, the method for montorng the crowd s almost manual. Ths method requres a huge amount of work to montor and the attenton of survellance personnel wll dstract wth the growth of the montorng tme, t s lkely to cause bg delay and mstakes. So, all of whch makes real-tme montorng s much more sgnfcant. Crowd estmaton methods can be dvded nto four categores. Frstly, crowd analyss based on background removal technology. Daves [1] and Chow [2] have proposed mage processng method based on pxel statstcs, calculated the number of foreground pxels through background subtracton technque, and estmated crowd by the number of pxels. Although ths method s smple and effectve, t s neffectve for hgh crowd. Secondly, crowd analyss based on mage processng and pattern recognton technology. In ths category, texture features are wdely used. Marana et al. [3] [4] have presented the crowd estmaton method of texture analyss. The obvous mprovement of ths method s that t solves the problem of overlappng, so that hgh crowd can be estmated by ths method. However, the dsadvantage s obvous: the accuracy s very low for low crowd. Thrdly, crowd analyss based on nformaton fuson. Velastn et al. [5] used the Kalman flterng method to count number of people by the background removal technology and boundary technology. The last method s newly generated method. Antono [6] and Donatell [7] used the method of feature ponts, and acheved better results. For the defects of two classcal algorthms: pxel statstcs and texture analyss, ths paper comprehensvely utlzes both advantages, and reduces the mpact of perspectve dstorton by dvdng the regon of nterest. Ths method can mprove the accuracy n the whole range. Furthermore, we estmate the crowd sze for the hgh and extremely hgh crowd. Expermental results show that the proposed method can mprove the accuracy n the whole range. The rest of the paper s organzed as follows. In secton 2 we gve the proposed approach. Expermental results are gven n the secton 3. And n the secton 3, we compare our algorthm to two classc algorthms. Secton 4 concludes wth applcatons, lmtatons and future work. II. OUR METHOD Frstly, after nputtng vdeo, we extract the foreground mage through Gaussan mxture model, and then process foreground mage; the processng ncludes flterng nose through medan flterng and morphologcal operatons. Secondly, estmate the number of people after dvdng the regon of nterest. The specfc statstcal methods are: gve a prelmnary judgment for the crowd do: /jsw

2 758 JOURNAL OF SOFTWARE, VOL. 9, NO. 3, MARCH 2014 through the number of foreground pxel. For dfferent, we use the method of pxel statstcs or GLCM texture analyss to extract crowd feature. And then use lnear regresson to estmate the number of the crowd. Fnally, add the number of each regon and then estmate crowd. It s worth mentonng that we estmate the crowd sze for the hgh and extremely hgh crowd. A. Pxel feature The property of the pxel statstc s the earlest feature to be used for crowd estmaton, and t s a very effectve feature. The basc dea of ths algorthm s: the denser crowd, the greater proporton of the foreground mage. Researchers consdered that there s a lnear relatonshp between the number of foreground pxel and number of people n the scene. Pxel features usually are: the foreground mage area, permeter, and edge pxels, and so on. Pxel statstcal algorthm s relatvely ntutve, easy to understand, low computatonal complexty, the relatonshp between the number of people and pxel feature s relatvely smple after preprocessed, easy to tran, and the generalzaton ablty of classfer or functon relatonshp s very well after tranng. However, the pxel statstcal algorthm has some problems: foreground mage segmentaton algorthm s not deal, and needs to correct the weght of extracted pxel due to the mpact of perspectve dstorton, has bad result n hgh crowd. In ths paper, ths pxel statstcal method s used to gve the ntal judgment of crowd, and estmate the crowd of extremely low, low, and medum. B. Texture feature The pxel s very mportant feature among crowd estmaton, but the accuracy s very low for more serous occluson area. To solve ths problem, Marana proposed texture analyss algorthm. Dfferent crowd has dfferent texture pattern for texture analyss. Images of low crowds show coarse texture, whle mages of hgh crowds show fne texture. The calculaton of GLCM texture features s a common and effectve method. In ths paper, ths texture method s used to estmate the crowd of extremely hgh and hgh. GLCM s a second-order statstcal method, whch can be thought of as second-order jont condtonal probablty functon p (, j dθ, ). Descrbe the mage along a certan drecton θ, separated by a certan dstance as the element d, the frequency of gray level of the pxel and j that s elements of the matrx. Where, j = 0,1,2,..., N 1 and N s gray level of the mage. Generally, as data of GLCM s very large, GLCM s not used drectly as texture features durng the feature classfcaton. Some researchers buld some statstcs based on ts classfcaton as texture features. Marana [8] used dfferent mage texture features and classfer made a detaled study for such an estmaton method based on classfcaton, drew the followng concluson: the best classfcaton results provded by the two GLDM descrptors: Contrast 0 and Homogenety 0, acheved the correct classfcaton accuracy of 85.5%. So ths paper selects these two descrptors to estmate the crowd number. Contrast: reflects the mage clarty and the depth extent of texture groove. The deeper the texture grooves, the greater the contrast s and the vsual effect more clearly as well. In GLCM, the larger the value drfts away from the dagonal elements, the greater the contrast s. Low crowd has hgh contrast than hgh crowd. So the varaton of contrast can represent the nformaton. Contrast s defned as, N 1N 1 2 ( ) (,, ) = 0 j= 0 Con = j p j d θ. (1) Homogenety: reflects the homogenety of mage texture, and measures how much local texture change. The large value llustrates that texture lack of change between dfferent regons and local s very homogeneous. Homogenety s defned as, N 1N 1 p (, j d, θ ) Hom =. (2) 2 = 0 j= 0 1+ j C. Defnton of Classfcaton Polus [9] proposed crowd from low to hgh s dvded nto fve levels. In ths paper, we reference to ths defnton, crowd levels are defned as shown n TableⅠ. D. Our Proposed Method Fgure 1 shows the detals of proposed method n ths Classfed level Crowd boundary (people) TABLE Ⅰ CROWD DENSITY OF EACH CATEGORY low Low Moderate Hgh hgh >100 paper. In ths method, 1) Capture vdeo, and use Gaussan mxture model to extract vdeo foregrounds. For the foreground mage, we use method of bnary process, nose elmnaton by medan flterng, and morphologcal operaton. 2) Set the regon of nterest. Snce the presence of abnormal projecton, especally n the process of large-scale montorng, the effect of abnormal projecton s partcularly evdent brought by the perspectve effect. To solve ths problem, ths paper dvdes nto four dfferent sub-regons for each scene mage. The sub-regon dvson effect s showed n Fgure 2.

3 JOURNAL OF SOFTWARE, VOL. 9, NO. 3, MARCH Fgure 1. Our method. N s the total number of foreground pxels for the -th regon. W s the wdth and H s the heght of regon. T s the threshold of the -th regon. We set T1 = 0.17, T2 = Snce n the selected vdeo, the number of regon 3 and regon 4 s very low, we do not need to gve the ntal judgment of crowd. We use pxel statstcal method to estmate the number of people n the two regon drectly. If the -th regon satsfes equaton (3), we use pxel statstcal method to estmate the number of people n ths regon, skp to step 4). Otherwse we use texture analyss method to estmate the number of people n ths regon, skp to step 5). 4) Pxel statstcal method. For a partcular regon, statstcs the total foreground pxel of ths regon, then the number of people n ths regon can be calculated accordng to the correspondng fttng straght lne. The fttng straght lne s traned by method of a lnear regresson. Accordng to number of foreground pxels and count the true number of people n ths regon artfcally for each regon of tranng samples, usng the least squares method, fttng out four straght lnes that the number of pxel corresponds to the number of people. 5) Texture analyss method. For a partcular regon, statstcs two texture descrptors (Contrast 0 and Homogenety 0 ) of ths regon, then the number of people n ths regon can be calculated accordng to the correspondng fttng straght lne. The fttng straght lne, whch s traned by method of multple lnear regressons. Accordng to the two texture descrptors and count the true number of people n ths regon artfcally for each regon of tranng samples, fttng out straght lnes that the two texture descrptors correspond to the number of people by the least square method. 6) Add the number of the four regons through step 4 and 5; the result s the number of people n a scene. From TableⅠ, we can know that the number of people belongs to whch level. If the scene level s hgh or extremely hgh, we contnue to estmate the crowd sze of the scene. Such as hgh scene s or people, extremely hgh scene belongs to or more than 120 people. Fgure 2. The mage drawng of dvded regon. 3) The ntal judgment of crowd. N /( W* H) T. (3) Ⅲ. EXPERIMENTAL AND COMPARATIVE RESULTS The test vdeo s captured n the rush hour after classes. In the course of the experment, each regon selected 40 mages as tranng samples for dfferent crowd denstes. And 160 mages of each were selected as the test samples. The 160 mages of hgh nclude and people each 80 mages, and more than 120 people each 80 mages n the 160 mages of extremely hgh. We assess dfferent performance

4 760 JOURNAL OF SOFTWARE, VOL. 9, NO. 3, MARCH 2014 characterstcs by comparng our expermental result wth calbraton results obtaned by manual. A. The software mplementaton The software was carred out n the Vsual Studo 2008 platform, usng C++ programmng language and addtonal open source lbrary of vdeo processng: OpenCV lbrary. The nterface can see from Fgure 3. level low TABLE Ⅱ RESULTS USING PROPOSED METHOD Number of test Number of samples wth test samples correctly classfed Accuracy testng (%) Low Moderate Hgh hgh TABLE Ⅲ CROWD SIZE RESULTS OF HIGH DENSITY AND EXTREMELY HIGH DENSITY Fgure 3. The software nterface. Crowd sze Number of test samples Number of test samples wth correctly classfed Accuracy testng (%) B. The sample tranng results of pxel statstc feature For each regon of 40 tranng samples, count the true number of people n ths regon artfcally, ft out four straght lnes that the number of pxel can correspond to the number of people by the least square method. Equaton of fttng straght lne s: Y = * N (4) Y 1 1 = * N (5) 2 2 Y = * N (6) Y 3 3 = * N (7) 4 4 N s the total number of foreground pxel for the -th regon, Y s the number of people n ths -th regon. = 1, 2, 3, 4. Statstcs the total foreground pxel of each regon, then the number of people n ths regon can be calculated accordng to the correspondng fttng straght lne. C. The sample tranng results of texture statstc feature In ths paper, we select two descrptors (Contrast 0 and Homogenety 0 ) of texture feature. Count the true number of people n ths regon artfcally for each regon of 40 tranng samples, ft out two straght lnes that the two descrptors can correspond to the number of people by the least square method. Equaton of fttng straght lne s: > TABLE Ⅳ COMPARISON BETWEEN OUR PROPOSED METHOD AND THE METHOD OF ONLY USE PIXEL STATISTICAL FEATURE OR TEXTURE ANALYSIS FEATURE level low Accuracy testng of pxel statstcal method (%) Accuracy testng of texture analyss method (%) T = * X * X (8) T = * X * X (9) Accuracy testng of proposed method (%) Low Moderate Hgh hgh

5 JOURNAL OF SOFTWARE, VOL. 9, NO. 3, MARCH Statstcs the two descrptors of each regon, then the number of people n ths regon can be calculated accordng to the correspondng fttng straght lne. D. The expermental results We select 160 test samples mages for each crowd, the estmaton of the expermental results are shown n Table Ⅱ. For hgh and extremely hgh, the crowd sze estmaton can be seen from Table Ⅲ. E. Compare wth classcal algorthms We compare our method wth the method of only use pxel statstcal feature [1] or texture analyss feature [4]. Ther expermental results are presented n Table Ⅳ. As we can see from Table Ⅳ, compare wth the smple use of pxel statstcal feature or texture feature, our proposed method has a relatvely hgh accuracy n the whole range. The reason s that we combne the advantages of both. Ⅳ. CONCLUSION Ths paper proposes an approach for crowd estmaton, whch combnes the pxel statstcal feature and texture feature. The proposed method removed background wth Gaussan mxture model and gave a prelmnary judgment for the crowd through pxel feature, meanwhle reduced the mpact of perspectve dstorton by dvdng the regon of nterest. The texture features were extracted usng GLCM, and selected Contrast 0 and Homogenety 0 as texture feature. Expermental and comparatve results show that the method s an effectve, unversal method whch can be used n a real-tme crowd estmaton system. And ths paper estmated the crowd sze for hgh and extremely hgh, whch was more conducve to group events analyss. Certanly, there s stll much room to mprove the accuracy. If the mage contans crowd shadow and reflectve surface, t mght lead to msclassfcaton. In the future, we wll accelerate foreground detecton approach and try to elmnate shadow nose n the mage. ACKNOWLEDGMENT Ths work s supported by the Natonal Hgh Technology Development 863 Program of Chna (No.2011AA01A107) and the Zhejang Provncal Techncal Plan Project (No. 2011C13008). REFERENCES [1] A.C. Daves, J.H. Yn, S.A. Velastn. Crowd montorng usng mage processng. Electroncs and Communcatons Engneerng Journal, February, pp , [2] Chow T. W. S. Cho, S. Y. and C. T. Leung. A neural based crowd estmaton by hybrd global learnng algorthm. IEEE Trans on Systems, Man, and Cybernetcs, pp , [3] Da Fontoura Costa L. Lotufo R. Velastn S Marana, A. Estmatng crowd wth Mnkowsk fractal dmenson. Proceedngs of IEEE Internatonal Conference on Acoustcs, Speech, and Sgnal Processng, pp , [4] Velastn S. Costa L. Lotufo R. Marana, A. Automatc estmaton of crowd usng texture. Safety Sc, 28(3): ,1998. [5] Velastn, S., Yn, J., Daves, A., Vcenco-Slva, M., Allsop, R., Penn, A.: Automated measurement of crowd and moton usng mage processng. Road traffc montorng and control, In: Seventh Internatonal Conference, pp , [6] Antono Albol, Mara Jula Slla, Alberto Albol and Jos e Manuel Moss. Vdeo Analyss usng Corner Moton Statstcs. Proceedngs 11th IEEE Internatonal Workshop on PETS, Mam, June 25, [7] Donatello Conte, Pasquale Fogga, Gennaro Percannella et al. A Method for Countng Movng People n Vdeo Survellance Vdeos. EURASIP Journal on Advances n Sgnal Processng Volume [8] A. N. Marana, L. F. Costa, R. A. Lotufo, and S. A. Velastn. On the effcacy of texture analyss for crowd montorng. Computer Graphcs, Image Processng, and Vson, pp ,1998. [9] Schofer J. Ushpz A. Polus, A. Pedestran Flow and Level of Servce. J. Transportaton Eng,109(1):46-56, [10] Z Ye, Jnqao Wang, Zhenchong Wang, Hanqng Lu. Multple features fuson for crowd estmaton. Proceedng ICIMCS '12 Proceedngs of the 4th Internatonal Conference on Internet Multmeda Computng and Servce, pp , [11] SUBBURAMAN V B, DESCAMPS A, CARINCOTTE C. Countng People n the Crowd Usng a Generc Head Detector[C]. Proceedngs of 2012 IEEE 9th Internatonal Conference on Advanced Vdeo and Sgnal-Based Survellance (AVSS): September 18-21, Bejng, Chna, pp , Mng Jang He receved the B.S. degree and M.S. degree n scence n 1996 and 2001 respectvely, and Ph.D. degree n Computer Scence n 2004, all from Zhejang Unversty, Chna. He s currently a Professor n college of Computer Scence, Hangzhou Danz Unversty, Chna. Hs research nterests nclude network vrtualzaton, Internet QoS provsonng, and network multmeda processng. Jngcheng Huang He receved hs BSc n Software Engneerng from Hangzhou Danz Unversty n Currently he s a master student n ths unversty. Hs prmary research area focuses on mage and vdeo processng, mage segment. Xngq Wang He receved hs Bachelor and Master degree from Harbn Insttute of Technology n 1997 and 1999, respectvely, and Ph.D degree from Zhejang Unversty n He s an assocate professor n college of Computer Scence, Hangzhou Danz Unversty, Chna. All hs major are Computer Scence. As a researcher, he vsted CERCIA, Unversty of Brmngham, UK from 2005 to Hs research nterests nclude machne learnng, data mnng and multmeda content analyss. Jngfan Tang He receved the Ph.D. degree n Computer Scence n 2005 from Zhejang Unversty, Chna. He s currently an Assocate Professor n college of Computer Scence, Hangzhou Danz Unversty, Chna. Hs research nterests

6 762 JOURNAL OF SOFTWARE, VOL. 9, NO. 3, MARCH 2014 nclude network vrtualzaton, qualty assurance, process mprovement and legacy system re-engneerng.. Chunmng Wu He receved the B.S. degree, M.S. degree and Ph.D. degree n Computer Scence from Zhejang Unversty, Chna, n 1989, 1992 and 1995 respectvely. He s currently a Professor n college of Computer Scence, Zhejang Unversty, and the drector of NGNT laboratory. Hs research felds nclude Network Multmeda processng, reconfgurable network technology, network vrtualzaton and artfcal ntellgence