Zhang Ning 1,2,, Zhu Jin-fu 1* 1 College of Civil Aviation of Nanjing, University of Aeronautics &

Transcription

1 [Type text] [Type text] [Type text] ISSN : Volume 0 Issue 2 BoTechnology 204 An Indan Journal FULL PAPER BTAIJ, 0(2), 204 [ ] A research on the arport montorng ntellgent recognton of terrorst volent actons Zhang Nng,2,, Zhu Jn-fu * College of Cvl Avaton of Nanng, Unversty of Aeronautcs & Astronautcs Nanng, 200, (CHINA) 2 Guangzhou Cvl Avaton College, Guangzhou, 50403, (CHINA) ABSTRACT It s very mportant to dentfy terrorsts behavoral features for the sake of arport securty. Generally, the tradtonal method s targeted at personnel recognton of specfc features of fxed scenes. The nonrestrctve characterstc of arport regons and the dsguse of volent terrorsts are left unconsdered. As a result, the recognton evolves nto a hgh dmensonal ssue wth ncreased dffculty n recognton. Ths paper presents a proecton feature algorthm to dentfy the behavoral features of terrorsts n an ntellgent way. The researchers reconstruct the features of the orgnal collected montorng mages. Durng reconstructon, the optmal cost functon s selected to provde a proper data bass for accurate recognton. The reconstructed two-dmensonal mages are proected on the one-dmensonal plane and transformed nto the one-dmensonal recognton ssue. The recurrence relaton s appled to categorze and recognze related volent terrorst actons wth enhanced recognton accuracy and effcency. KEYWORDS Arport; Intellgent recognton; Terrorst volent actons. Trade Scence Inc.

2 6792 A research on the arport montorng ntellgent recognton of terrorst volent actons BTAIJ, 0(2) 204 INTRODUCTION To expand the volent terrorst nfluence, volent terrorsts always choose to perform terrorst volence [] on publc stes, such as the ralway staton and arport wth great flow densty. Any occurrence of volent terrorst ncdents at the nternatonal hub arport wll exert serously adverse nternatonal nfluence. Therefore, currently t s urgent for the cvl avaton ndustry to meet the crucal demand for effectve preventon from volent terrorsm n arport regons [2]. The arport regonal montorng system s able to montor the crowd wthn arport regons n real tme and dynamcally [3], provdng mportant techncal support for copng wth emergences [4]. It has been a hot ssue of researches on arport securty management to utlze the arport regonal montorng system for ntellgent recognton of crowd actons nvolvng terrorsm [5]. At the present stage, there are three man methods for ntellgent recognton of crowd actons nvolvng volent terrorsm n arport regons:. the method based on edge detecton algorthm; 2. the method based on feature fuson; 3. the method based on Gaussan model. The last one based on Gaussan model s employed the most frequently [6]. However, as often dscovered durng the collecton of crowd volent actons through the arport regonal montorng system, the fdelty of the collected volent terrorst acton mages s relatvely low, because mages tend to be mpacted by a varety of factors, ncludng llumnaton, the montorng angle, the ntentonal dsguse of terrorsts, etc. The tradtonal methods for personnel recognton are based on the specfc features of fxed scenes wthout consderng the nonrestrctve characterstc of arport regons and the dsguse of terrorsts. As the recognton becomes a hgh dmensonal ssue of categorzaton and recognton, the crowd volent actons are dentfed less accurately wth decreased tmelness. Hereby the researchers present a method based on proecton feature algorthm for ntellgent recognton of crowd volent actons n arport regons. The features of the orgnal collected montorng mages are reconstructed. Durng reconstructon, the optmal cost functon s selected. The reconstructed two-dmensonal mages are proected on the one-dmensonal plane and transformed nto the onedmensonal recognton ssue. The volent actons are hence dentfed wth less tme but mproved accuracy. RECOGNITION PRINCIPLE OF TERRORIST ACTIONS IN AIRPORT REGIONS Subsequent to the collecton of crowd acton mages through the arport regonal montorng system, the collected mages should be pretreated n the frst place to elmnate the nfluence of nose; recognton should be completed accordng to fxed features. The prncple s stated as follows: The arport crowd acton mages are dvded nto grds wth the same sze. The gray values of dfferent grds are extracted. The mean gray value of mages s used as the ntal feature. The collected crowd acton mages are set to be dvded nto K L grds ζ kl,. These grds can overlap. All grds are n the same sze. The ntal feature of crowd acton mages s X = [ x, x2,..., x K L ]. The mean gray value of grds can be descrbed by the followng formula: x = I ( l ) K+ k Z dx dy Γ kl, () The pror probablty of the mean gray value of grds s set as px. ( ) The followng formula can be used to descrbe the posteror probablty of the mean gray value. Pw ( x) = p( x w) P( w) px ( ) (2)

3 BTAIJ, 0(2) 204 Zhang Nng and Zhu Jn-fu 6793 In the formula, p(w) s the revsed probablty. Wth the above method, the collected crowd acton mages can be pretreated to remove the nfluence of nose and ensure the accurate recognton of volent terrorst actons. Through the above pretreatment of the collected crowd acton mages, the standard mages to be recognzed are obtaned. Then the features of crowd actons are dentfed. The specfc methods are shown as follows: The followng formula s used to extract the features of crowd acton mages: l l R( θ) = L( Xt, θ) = ln p( Xt, θ) l l t= t= (3) The followng Gaussan model s establshed n lght of the above extracton results: P( X t, θ ) = M α P ( X ) k = k α P ( X ) k t t (4) P ( X) s used to descrbe the D-dmensonal Gaussan dstrbuton of the crowd mage features andα s ts weght. The mages to be recognzed are classfed by a classfer through the constructed Gaussan model. The classfcaton formula s lsted as follows: * n = arg MaxP( k X ) (5) * In the formula, n refers to the recognton result of crowd acton features. Through the applcaton of the above method, the features of crowd acton mages are extracted; the Gaussan model s constructed to classfy the crowd actons; fnally the acton features of terrorst suspects are dentfed n an ntellgent way. Nevertheless, n the tradtonal methods used for personnel recognton based on specfc features of fxed scenes, the nonrestrctve characterstc of arport regons and dsguse of volent terrorsts are not taken nto consderaton. The recognton changes nto a hgh dmensonal ssue wth lowered accuracy and tmelness of crowd volent terrorst acton recognton. A VIOLENT TERRORIST PERSONNEL RECOGNITION METHOD BASED ON PROJECTION FEATURE ALGORITHM When the tradtonal methods are adopted for ntellgent recognton of crowd volent terrorst actons n arport regons, the collected mages are affected by the weather, llumnaton, the montorng angle and the ntentonal dsguse of volent terrorsts, etc. Thus the relablty of crowd acton mages and the recognton accuracy declne. Thereupon the researchers put forward a method based on proecton feature algorthm for ntellgent recognton of crowd volent terrorst actons n arport regons. Reconstructon of crowd acton mage features D Set N samples of crowd acton features, V, V 2,..., V N ; V R, =, 2,..., N. The followng formula can be used to calculate the smlarty between any two data samples of crowd acton features, V and V. S = / V V (6)

4 6794 A research on the arport montorng ntellgent recognton of terrorst volent actons BTAIJ, 0(2) 204 Here, s the norm of the two sample data of crowd acton features. The followng formula can be used to calculate the average smlarty between V and any other sample data of crowd acton features: M = N V V (7) In the formula, N and. M s used to descrbe the threshold of neghbour selecton. If the smlarty between V, the crowd acton features, and V, any crowd acton feature sample data, s larger than M, then V s the close neghbour of V. If such smlarty s not larger than M, then V s not the close neghbour of V. The formula (6) s only a part of the hyperbolc curve located at the frst quadrant, namely the closer t s to V, the sample pont of crowd acton features, the larger S s; the farther away t s from V, the sample pont of crowd acton features, the smaller S s. Hence, the M value n the formula (7) should be closer to V to avod the return crcut problem resultng from the overly large neghbour number of the crowd acton feature samples. The formula (6) s a monotone decreasng functon, so the number of neghbours of all crowd acton feature samples should be determned by the dchotomy method as specfcally shown n the followng steps: ) As to any crowd acton feature samplev, the Eucldean dstances betweenv and the rest of crowd acton feature samples are sequenced n lne wth ther sze. The ntal values are set as λ mn = and λ max = N ; 2) λ = ( λmn + λmax )/2s set. If the condton, S [ ] M S λ+ [ λ], s met, then turn to the step 4) drectly; otherwse turn to the step 3) where[ λ] s the nteger value of λ taken downward; 3) As to the smlarty betweenv, the crowd acton feature sample, and V [ λ ], f the smlarty value s larger than M, then set λmn = λ ; otherwse, set λmax = λ and turn to the step 2); 4) Determne λ = λ as the scope of neghborhood of V, the crowd acton feature sample, and conclude the calculaton. The calculaton method for the optmal cost functon durng mage reconstructon When the close neghbours of all crowd acton feature samples are determned, such sample mages can thus undergo the lnear reconstructon. In case of the reconstructon error durng mage reconstructon, the quantzaton should be mnmzed. The followng formula can be used to descrbe the cost functon of reconstructon error. λ () = ε ( W) = mn V W V λ () mn W ( V V) WWkG k = = = 2 (8) Here,ε refers to the lnear reconstructon error under the crcumstance wherev, the crowd acton feature sample pont, has k close neghbours, V, V2,..., V k, used for sample mage reconstructon. W and

5 BTAIJ, 0(2) 204 Zhang Nng and Zhu Jn-fu 6795 G are used to descrbe the reconstructon weght n the structural form of Gram matrx. The number of close neghbours used for reconstructon s dfferent for each crowd acton feature samplev. Durng the mage reconstructon, each column of non-zero pxels n the matrx W dffers greatly, namely the column ofw may have non-zero pxel values of λ only. SupposeG k s postve defnte, there must be an nverse matrx G k ; the optmal reconstructon functon of the optmal crowd acton feature mages can be acqured, as shown n the followng formula: W λ λ λ () () = (Q m) / (Q pq) m= p= q= (9) As to () T () Qm = ( V V) ( V Vm) for allw, the upper lmt value of ts cumulatve sum s K. () Whle eachw s calculated, the upper lmt value of ts cumulatve sum λ may be dfferent. Intellgent recognton of crowd volent terrorst actons The steps to dentfy the feature mages of crowd volent terrorst actons through the proecton feature algorthm should be taken as follows: frst, the two-dmensonal mages of crowd actons are proected on the one-dmensonal plane to transform the two-dmensonal ssue nto the one-dmensonal recognton; then the tme of dentfyng crowd volent terrorst acton features wll decrease accordngly. On such bass and n accordance wth the recurrence prncple, the operaton amount of crowd volent terrorst acton feature recognton at each poston and the recognton tme to meet the real-tme requrement of recognton are reduced. The one-dmensonal gray proecton of crowd acton mages s to sequence the gray values n lnes by takng the top left corner of the template maget as the orgn and to calculate the mean by the followng formula: M V( k) = T( m, k), k = 0,,2,..., N M m = (0) Gxys (, ) set as an M N-szed crowd acton mage to be recognzed (G for short); g(x, y) s an m n -szed crowd acton template mage (g for short). g, s used to descrbe the sub-mage, of the same sze wthg, extracted from the mage G based on the orgn (, ). The followng formula can descrbe the matrx to whch the sub-mage corresponds: g (, ) ( st, ) = f( + s. + t ) s =,2,...,m, t =,2,...,n () ρ(, ) s set to be capable of descrbng the correlaton coeffcent between g, andg. After the acquston of the one-dmensonal gray proecton of crowd acton mages, the onedmensonal matrx M n the length of n can also be obtaned. Durng the crowd acton feature recognton, (M m+ ) (N n+ ) sub-mages are extracted sequentally to go through the onedmensonal gray proecton; then(m m+ ) (N n+ ) one-dmensonal matrces n the length of n can be obtaned. The followng formula can be used to descrbe the one-dmensonal correlaton coeffcent of crowd acton features: R (, ) = cov( g, g), P (, ) p (2)

6 6796 A research on the arport montorng ntellgent recognton of terrorst volent actons BTAIJ, 0(2) 204 G s set to be the gray mage of crowd actons; T s set to be the template mage of volent terrorst actons; G, ( k) s the one-dmensonal matrx correspondng to the gray proecton of the submage of the same sze wth T and extracted based on the orgn(, ) from G. Then the followng recurrence relaton comes nto exstence: + = + β α αg 2 P(,) P(,) 2, (3) In the formula: α = G(, ) ( n) G(, ) () n β = G( +, ) ( n) G(, ) () n (4) Wth the above-llustrated method, the two-dmensonal mages of crowd actons are proected on the one-dmensonal plane to cut down the tme to dentfy the crowd volent terrorst acton features. In accordance wth the recurrence prncple, the operaton amount of crowd volent terrorst acton feature recognton at each poston s reduced to meet the real-tme requrement of recognton. EXPERIMENTAL RESULTS AND ANALYSIS Smulaton data and envronment An experment was carred out to demonstrate the effectveness of the algorthm presented by ths paper. The experment data were sourced from the authentc crowd volent terrorst mage database of the arport regonal montorng system n some large hub arport. 000 mages of crowd volent terrorst actons were frst selected randomly and dvded nto two groups wth equal quantty. One group served as the tranng sample, namely the template mages of crowd volent terrorst acton features; the other group served as the testng sample, namely the crowd acton mages to be recognzed. The expermental envronment was the Wndows 8. operatng system wth Pentum (R) Dual-Core CPU T GHZ. The expermental envronment was constructed by VC++6.0 and Matlab 7.5. Comparson of convergence speeds of dfferent algorthms To demonstrate the real-tme effect of the algorthm presented by the paper on the arport crowd volent terrorst acton recognton, a comparson was requred between the respectve recognton convergence speeds of the tradtonal algorthm and the algorthm presented by the paper. The recognton convergence curves of dfferent algorthms were shown n Fgure. Fgure : Comparson of convergence speeds of the tradtonal algorthm and the mproved algorthm

7 BTAIJ, 0(2) 204 Zhang Nng and Zhu Jn-fu 6797 The above expermental result revealed an obvously faster recognton convergence speed of the algorthm presented by the paper compared wth that of the tradtonal algorthm. It fully proved the effectveness of the mproved algorthm, because the feature reconstructon of the collected arport regonal crowd acton mages elmnated the nfluence of nterference factors, transformed the twodmensonal feature recognton nto the one-dmensonal recognton ssue, reduced the recognton tme and ncreased the recognton speed. Comparatve analyss of recognton results from dfferent algorthms For further demonstratng the effectveness of the algorthm presented by the paper, the recognton results of crowd volent terrorst feature mages obtaned through dfferent algorthms were compared. Durng the recognton based on the tradtonal algorthm, the crowd volent terrorst acton mages were dvded nto grds wth the same sze. The gray values wthn dfferent grds were extracted. The mean gray value of mages was used as the ntal feature to construct the Gaussan model. Eventually the dentfed crowd actons were categorzed by the classfer. Durng the recognton based on the algorthm presented by the paper, the frst step was feature extracton of the crowd volent terrorst mages n the mage lbrary. The threshold M for neghbour selecton was set as 0.6 for the feature extracton of volent terrorst actons V. The sample mages were proected on the onedmensonal plane. Subsequently through the gray normalzaton, the standard mages to be recognzed were thus obtaned. These mages wth the same mean and varaton were then classfed and dentfed n terms of volent terrorst actons. The recognton results based on the tradtonal algorthm were shown n Fgure 2. Fgure 2: Recognton results based on the tradtonal algorthm Fgure 3: Recognton results based on the algorthm presented by the paper It can be seen from the above recognton results, the tradtonal algorthm, wthout consderng the multple factors such as llumnaton, montorng angle and ntentonal dsguse of volent terrorsts, led to the declne n recognton accuracy. Whereas the mproved algorthm acheved hgher recognton accuracy by extractng the orgnal mage features and elmnatng the external nterference. The expermental results were shown n TABLE, Fgure 4 and Fgure 5 based on the above expermental data. The radar dagrams (Fgure 4 and Fgure 5) clearly dsplayed sgnfcant mprovement n the effcency and tme for the crowd volent terrorst acton recognton n arport regons based on the new method. Relatve to the tradtonal method, the new one mproved the recognton effcency by an average 23% and the recognton speed by an average 2.2 seconds. The superorty of the algorthm presented by the paper was fully proved.

8 6798 A research on the arport montorng ntellgent recognton of terrorst volent actons BTAIJ, 0(2) 204 Varetes of Volent Terrorsm TABLE : Comparatve analyss of expermental results Number of Samples Recognton Rate (%) Tme (ms) Method Method 2 Method Method 2 Knfe holdng % 85% 2 3 Httng % 94% 25 Kckng % 90% 32 6 Holdng n the arms % 97% 26 8 Strkng wth the foot % 83% 27 2 Fgure 4: The radar dagram of recognton rate comparson between two methods Fgure 5: The radar dagram of tme and effcency comparson between two methods CONCLUSION The researchers n the paper proposed a method based on proecton feature algorthm for ntellgent recognton of crowd volent terrorst actons. The features of the orgnal collected montorng mages were reconstructed. The optmal cost functon was selected durng reconstructon. The two-dmensonal reconstructed mages were proected on the one-dmensonal plane. The transformed one-dmensonal recognton ssue reduced the recognton tme and mproved the recognton accuracy. The smulaton experment demonstrated the dstnct advantages of such new algorthm compared wth the tradtonal one. It furthered the accuracy and real-tme effect of recognton and provded techncal support for the securty n arport regons. REFERENCES [] Bruce Hoffman, Gordon H. Mccormck; Terrorsm, Sgnalng and Sucde Attack, [2] Ignaco Sánchez-Cuenca; Domestc terrorsm: the hdden sde of poltcal volence, Annual Revew of Poltcal Scence, 2, [3] Wuernsha Abdurey; Volent terrorst personnel postonng wth crcular array based on tme delay estmaton of vdeo pxel gray value, Bulletn of Scence and Technology, 6, (204). [4] Todd Sandler; Collectve acton and transnatonal terrorsm, The World Economy, 26(6), (2003). [5] Ethan Bueno de Mesquta; Conclaton, counterterrorsm and patterns of terrorst volence, Internatonal Organzaton, 59(), (2005). [6] Bruno S. Freya, Smon Luechnger; How to fght terrorsm: alternatves to deterrence,defence and peace economcs, 4(4), (2003).