Real-Time Motion Detection Using Low-Resolution Web Cams

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1 24 Int'l Conf. Informaton and Knowledge Engneerng IKE'5 Real-Tme Moton Detecton Usng Low-Resoluton Web Cams Mark Smth Unversty of Central Arkansas Conway, Arkansas Abstract The analyss of moton detecton wthn vdeo sequences has ncreased dramatcally n recent years. One prmary applcaton of moton detecton has been survellance systems that utlze vdeo cameras. In recent years, a new nterest n survellance systems usng lower resoluton webcams has ncreased requrng addtonal consderatons not found n hgher resoluton systems. Ths paper examnes ths problem by mplementng a complete survellance system usng the popular Tenvs JPT35W web camera. The moton detecton algorthms are ntally mplemented usng the standard MPEG-7 descrptors commonly used for analyzng vdeo sequences and creatng vdeo database systems. The results of a sngle MPEG-7 descrptor are generally not adequate for detectng all types of moton under varyng lghtng condtons. Ths work ntroduces a system that ntellgently combnes multple descrptors n a votng algorthm that provdes more accurate results than merely usng one such descrptor. An analyss on the most benefcal descrptors s presented wth a rankng provded for these descrptors. Results are provded for real tme vdeos collected from varous locatons undergong a wde range of lghtng condtons over a 24 hour tme perod. An Phone App has also been mplemented allowng access to the system remotely.. Introducton Vdeo survellance has become one of the most mportant applcatons of moton detecton and vdeo processng systems have provded numerous applcatons for dentfyng moton [,4]. Many popular algorthms exst usng technques smlar to dentfyng hard-cuts (.e., nstantaneous changes) n moton pcture flms. These nstantaneous changes result n adjacent frames of the dgtal vdeo sequence undergong sgnfcant and easly recognzable changes often detected by correspondng pxel analyss usually consstng of color dfferences. These algorthms have been margnal at best when appled to systems consstng of lower resoluton off-the-shelf webcams undergong varyng lghtng condtons. Webcams are commonly avalable to most consumers often makng them the camera of choce due ther accessblty and ease of use and ntegraton wth the overall computer system. The lower resoluton of the frames produced by these webcams often ntroduces nose that often results n many false dentfcaton of moton [3]. Ths work ntroduces a new moton algorthm robust enough to flter the nose from the lower resoluton mages as well as that ntroduced from the effects of lghtng condtons. Ths system was provded on MacOS and the moble app was mplemented for OS usng XCode. The system developed from ths research and presented n ths paper s separated nto the followng sectons: MPEG-7 Overvew Edge Hstogram Features Gabor Flter Features Parametrc Moton Features Moton Actvty Features Extracton of Features from Indvdual Frames n XML format Classfcaton algorthm used for Moton Detecton between adjacent frames Ths paper wll explore each of these tems and the subsequent algorthm mplementaton n detal. 2. MPEG-7 Overvew Usng a set of relable tools s a crtcal necessty n startng ths project. An mportant frst step s to ntegrate ths MPEG-7 s an ISO/IEC standard developed by MPEG (Movng Pcture Experts Group), the commttee that also developed the Emmy Award wnnng standards known as MPEG- and MPEG-2, and the standard[,4,7]. Mpeg- and MPEG-2 standards made nteractve vdeo on CD-ROM and Dgtal Televson possble. MPEG-4 provdes the standardzed technologcal elements enablng the ntegraton of the producton, dstrbuton and content

2 Int'l Conf. Informaton and Knowledge Engneerng IKE'5 25 access paradgms of the felds of dgtal televson, nteractve graphcs and nteractve multmeda [5]. There are many descrptors avalable wthn MPEG-7, but ths paper only focuses on two general category texture and moton. These features are selected due to ther reslence to lghtng and color changes that a scenes typcally undergoes durng dfferent transtons of a gven day. Applyng a hstogram equalzaton flter also mproves the results by accountng for these lghtng changes and restorng many edge detals lost from the dmmng of the mage. The central dea for ths algorthm s that texture and moton features are extracted from each frame of the vdeo and then compared n an ntellgent manner va a dscrmnant votng algorthm thus dentfyng f a gven scene has sgnfcantly changed [2]. Snce there s no camera moton, there s no need to frst segment the vdeo nto shots or ndvdual scenes; the webcam s provdng a contnuous scene for as long as the vdeo s sampled. The texture and moton features selected from the MPEG-7 descrptors are: Edge Hstogram Homogenous Texture (Gabor) Flters Parametrc Moton Moton Actvty Each of these features are dscussed n the sectons that follow and then combned nto an ntellgent votng algorthm used for dentfyng moton between adjacent frames. 3. Edge Hstogram Features The edge hstogram feature specfes the spatal dstrbuton of the followng fve edge types shown n Fg. (a) (b) (c) (d) Vertcal Horzontal Dagonal Dagonal Fg. Edge Hstogram Ths feature s selected as a canddate for computng ths texture measurement because of ts compact sze (5-dmensons) and ts very effcent algorthm mplementaton [9]. The edge hstogram s also one of the standard features used for vdeo retreval applcatons as descrbed n the MPEG-7 standards [2,5,6]. The algorthm utlzed n calculatng the edge hstogram features s descrbed as follows: a. The bnary mask for a chosen object s generated. The 256-level gray-scale mage s generated from the orgnal RGB color frame. The gray-scale value for each pxel s computed as R+ G+ B gray () 3 Where R, G, and B are the color components for each pxel extracted from the orgnal color mage. b. ext, a 4x4 mask correspondng to each edge category vertcal, horzontal, dagonal, and off-dagonal s convolved wth the object s gray-level regon. Ths convoluton process s expressed by equaton (2) as 3 3 h k m g (2) 0 j 0 Where mk s one of the 4 edge masks, g s the gray-level of a pxel assocated wth an object, and hk s the result of the convoluton. Boundary regons not fully enclosng the 4x4 mask are gnored n ths computaton. c. The edge type provdng the maxmum value resultng from ths convoluton s then noted. Ths value s referred to as hkmax n the dscusson that follows. d. If hkmax exceeds an emprcally determned threshold, the correspondng edge type s consdered detected and the regon s classfed to the edge mask whch generated the maxmum convoluton value hkmax. If hkmax does not exceed the threshold, the regon s classfed as a non-drectonal edge (Fg. (e)). e. Steps c,d, and e are repeated for all nonoverlappng 4x4 blocks of the regon s nteror. The four edge types and the nondrectonal edges are accumulated for the object resultng n a fve-bn hstogram. f. Steps c,d,e, and f are then repeated for all other objects n the frame. g. All steps (a-g) are then repeated for all frames n the sequence. Ths process results n a 5-dmensonal edge hstogram computed for each frame. The value EH gven n (3) s based on the normalzed Eucldean dstance of the edge hstogram computed between adjacent frames p and c s gven as: k

3 26 Int'l Conf. Informaton and Knowledge Engneerng IKE'5 ehp EH ehp ehc eh + eh p c (3) Where s the edge hstogram computed for eh the prevous frame, c s the edge hstogram EH computed for the current frame, and s the texture measurement of frame computed between adjacent frames. 4. Homogenous Texture (Gabor) Flters Homogeneous texture has emerged as an mportant vsual prmtve for searchng and browsng through large collectons of smlar lookng patterns [2]. An mage can be consdered as a mosac of homogeneous textures so that these texture features assocated wth the regons can be used to ndex the mage data. For nstance, a user browsng an aeral mage database may want to dentfy all parkng lots n the mage collecton. A parkng lot wth cars parked at regular ntervals s an excellent example of a homogeneous textured pattern when vewed from a dstance, such as n an Ar Photo. Smlarly, agrcultural areas and p vegetaton patches are other examples of homogeneous textures commonly found n aeral and satellte magery. Examples of queres that could be supported n ths context could nclude Retreve all Land- Satellte mages of Santa Barbara whch have less than 20% cloud cover or Fnd a vegetaton patch that looks lke ths regon. To support such mage retreval, an effectve representaton of texture s requred. The Homogeneous Texture Descrptor provdes a quanttatve representaton usng 62 numbers (quantfed to 8 bts each) that s useful for smlarty retreval [0]. The extracton s done as follows; the mage s frst fltered wth a bank of orentaton and scale tuned flters (modeled usng Gabor functons) usng Gabor flters. The frst and the second moments of the energy n the frequency doman n the correspondng sub-bands are then used as the components of the texture descrptor. The number of flters used s 5x6 30 where 5 s the number of scales and 6 s the number of drectons used n the mult-resoluton decomposton usng Gabor functons. An effcent mplementaton usng projectons and -D flterng operatons exsts for feature extracton. The Homogeneous Texture descrptor provdes a precse quanttatve descrpton of a texture that can be used for accurate search and retreval n ths respect. The computaton of ths descrptor s based on flterng usng scale and orentaton selectve kernels. A dagram llustratng the mplementaton of the Gabor Flter s shown below: Fg. 2 Gabor Flters allow the system to flter outlers or ncorrect ncdents from those newly occurrng events. 5. Parametrc Moton and Moton Actvty Parametrc moton models have been extensvely used wthn varous related mage processng and analyss areas, ncludng moton-based segmentaton and estmaton, global moton estmaton, mosackng and object trackng [6]. Parametrc moton models have been already used n MPEG-4, for global moton estmaton and compensaton and sprte generaton. Wthn the MPEG-7 framework, moton s a hghly relevant feature, related to the spatal-temporal structure of a vdeo and concernng several MPEG-7 specfc applcatons, such as storage and retreval of vdeo databases and hyperlnkng purposes. Moton s also a crucal feature for some doman specfc applcatons that have already been consdered wthn the MPEG-7 framework, such as sgn language ndexaton [3]. The basc underlyng prncple conssts of descrbng the moton of objects n vdeo sequences as a 2D parametrc model. A human watchng a vdeo or anmaton sequence perceves t as beng a slow sequence, fast paced sequence, acton sequence etc. The actvty descrptor captures ths ntutve noton of ntensty of acton or pace of acton n a vdeo segment [7]. Examples of hgh actvty nclude scenes such as goal scorng n a soccer match, scorng n a basketball game, a hgh speed car chase etc.. On the other hand scenes such as news reader shot, an ntervew scene, a stll shot etc. are perceved as low acton shots. Vdeo content n general spans the gamut from hgh to low actvty, therefore we need a descrptor that enables us to accurately express the actvty of a gven vdeo sequence/shot and comprehensvely covers the aforementoned gamut [8]. The actvty descrptor s useful for applcatons such as vdeo re-purposng, survellance and fast browsng, An example of moton actvty computed for a scene s shown n Fg. 3:

4 Int'l Conf. Informaton and Knowledge Engneerng IKE'5 27 V j fk f jk k 0 f + f j (4) Fg. 3 Moton Actvty 6. Extracton of Features The 4 MPEG-7 features are extracted n the form of vectors and stored n a fle formatted usng the XML Language. The vectors generated for each of the MPEG-7 features are shown below: 5 features for Edge Hstogram 62 features for Homogenous Texture 7 features for Parametrc Moton 5 features for Moton Actvty The vectors are formatted wthn an XML fle thus allowng for ease of organzaton and retreval. An example of the XML extracted for the above features are shown below: <?xml verson.0?> <frame name 4289 date :7:2 > <feature name EH /> <feature name HT /> (others) <//feature> <feature name PM /> (others) <feature name MA /> (others) </frame>>..(others follow) The features are utlzed n an algorthm consstng of a seres of equatons used n dentfyng the moton computed between adjacent frames. The next secton descrbes the algorthms used for classfyng the moton between adjacent frames. 7. Moton Identfcaton and Classfcaton. The moton and texture features are extracted for each frame as specfed n [2,3] are grouped nto correspondng vectors gven as equaton (4) gven below. The correspondng vector dstances are computed between adjacent frames (I and j) for each feature as shown below: where and j are the adjacent frames, and k s the kh feature and then compared by computng the ormalzed Eucldean dfference between each set. The mean for each vector set s next computed over each frame as shown below n 5: k 0 μ V k (5) The mean for each vector set s updated for each frame that s encountered. The standard devaton between all frames for a gven vector s then computed as shown below: 2 ( V μ) σ (6) The adaptve threshold between frames s used when classfyng the moton between adjacent frames. Moton s assgned to the second frame f the vector dfference s greater than the adaptve threshold gven n (7) as: Vj > 2σ + μ (7) Equatons (4) thru (7) are repeated for each of the 4 vector sets Edge Hstogram, Homogenous Text, Parametrc Moton, and Moton Actvty. Equaton (7) s computed for all vector sets and for all cases that (7) s true, s mantaned If 3 out of 4 of the vector sets are true, moton wll be assgned to the second frame. Ths result can be expressed n the followng equaton as: motonp+ Tc T 3 p cp + > (8) 0 The major assumpton for ths algorthm s that a seres of frames wth no moton are encountered frst before any moton occurs. The seres of non-moton frames are used for ntalzng the system and settng a baselne and therefore provdng tranng to ths system, so when moton s encountered, t s easly dentfed and classfed wth mnmum errors. 8. Testng and Results Ths system was ntally tested on a seres of standard MPEG vdeos contanng a seres of frames undergong moton. Table shown below llustrates

5 28 Int'l Conf. Informaton and Knowledge Engneerng IKE'5 the results of the system The 2 nd column contans the total number of frames processed, the 3 rd column ndcates the number of frames correctly classfed as ether moton/no moton, whle the 4 th column ndcates the number of frames ncorrectly classfed as ether moton/no moton. The Percent Correct s gven as the rght most column. Table Results Vdeo Total Correct False Percent Correct Happy % Granny Foreman % MotorCycle % ews % Most of the errors occur at the outlers ether when the moton occurs at the begnnng or the end of the vdeo clp. Also very slght moton s dentfed and classfed as moton. The system was also tested wth a low resoluton Tenvs JPT35W web cam producng an mage wth a 60x20 resoluton. The web cam was mounted n the author s offce and produced/transmtted mages every 2 seconds to a web server runnng ths system for analyss. The test was performed over a 24 hour perod where the web cam underwent a varety of lghtng condtons wth over 7000 frames transferred and tested. The results of the web cam tests were smlar to that of the standard vdeo wth a very hgh percentage of the vdeo clp classfed correctly as ether havng moton or not havng moton. The results appear very promsng llustratng the accuracy of ths system. The error rate s well wthn bounds and provdes users wth a very accurate moton detecton system for low-resoluton web cams. 9. References [] Y. Deng and B. S. Manjunath, Unsupervsed segmentaton of color-texture regons n mages and vdeo, IEEE Trans. on Pattern Analyss and Machne Intellgence, vol. 22, no. 6, pp , 200. [2] Ar Pressure: Why IT Must Sort Out App Moblzaton Challenges". InformatonWeek. 5 December [3] E. D. Gelasca, E. Salvador and T. Ebrahm, Intutve strategy for parameter settng n vdeo segmentaton, Proc. IEEE Workshop on Vdeo Analyss, pp , [4] MPEG-4, Testng and evaluaton procedures document, ISO/TEC JTC/SC29/WG, 999, (July 995). [5] R. Mech and M. Wollborn, A nose robust method for segmentaton of movng objects n vdeo sequences, ICASSP 97 Proceedngs, pp , 997. [6] T. Aach, A Kaup, and R. Mester, Statstcal modelbased change detecton n movng vdeo, IEEE Trans. on Sgnal Processng, vol. 3, no 2, pp , March 993. [7] L. Charglone-Convenor, techncal specfcaton MPEG- ISO/IEC JTC/SC29/WG MPEG 96, pp , June, 996. [8] MPEG-7, ISO/IEC JTC/SC29/WG2, 2207, Context and objectves, (March 998). [9] P. Detel,Phone Programmng, Prentce Hall, pp , [0] C. Zhan, X. Duan, S. Xu., Z. Song, M. Luo, An Improved Movng Object Detecton Algorthm Based on Frame Dfference and Edge Detecton, 4th Internatonal Conference on Image and Graphcs (ICIG), [] R. Cucchara, C. Grana, M. Pccard, Member and A. Prat, Detectng Movng Objects, Ghosts, and Shadows n Vdeo Streams, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 25, no. 0, pp , October, [2] F. Rothganger, S. Lazebnk, C. Schmd and J. Ponce, Segmentng, Modelng, and Matchng Vdeo Clps Contanng Multple Movng Objects, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 29, no.3, pp , March [3] el Day, Jose M. Martnez, Introducton to MPEG- 7, ISO/IEC/SC29/WG 4325, July, 200. [4] M. Ghanbar, Vdeo Codng an Introducton to standard codecs, Insttuton of Electrcal Engneers (IEE), 999, pp [5] L. Davs, An Emprcal Evaluaton of Generalzed Cooccurrence Matrces, IEEE Trans. Pattern Analyss and Machne Intellgence, vol 2, pp , 98. [6] R. Gonzalez, Dgtal Image Processng, Prentce Hall, 2nd edton, pp , 2002 [7] K. Castelman,Dgtal Image Processng, Prentce Hall, pp , 996.

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