Nondeterministic Finite Automaton Event Detection in Focusing Region. Sequence Analysis. Sequence Analysis. Feature Extraction. Feature Extraction

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1 y yy y Mult-Object Behavor Recognton by Selectve Attenton Toshkazu WADA y, Masayuk SATO yy,andtakash MATSUYAMA y ( ) (NFA) ( ), ( ) NFA,,. ( ) ( ),, ( ) ( ) ( ) y Department of Intellgence Scence and Technology, Graduate School of Informatcs, Kyoto Unversty, Yoshda- Honmach, Sakyo, Kyoto, 66- Japan yy Department of Electroncs and Communcaton, Graduate School of Engneerng, Kyoto Unversty, Yoshda-Honmach, Sakyo, Kyoto, 66- Japan ( ) ) ) (a)! (HMM) [], [], [4] HMM II II

2 '96/xx Vol. J79{D{II No. xx Sequence Analyss Feature Extracton (a) State Transton Model (HMM, etc.) Sequence Analyss Feature Extracton (b) Nondetermnstc Fnte Automaton Event Detecton n Focusng Regon (a):, (b):. Fg. Behavor recognton system. (a): Bottom-up system, (b): Bottom-up and Top-down system. lp readng [3] ' ' ( ) ( ) :! :! ( (b)) HMM (NFA) ) ) NFA NFA

3 .... ( ) NFA: (Q; q ; 6;;F) Q:,fq ; ; ;q m ;q rej g. (q ; ; ;q m ) q rej q m q m q acc q : 6: ( ) : (q; ) :Q6 7! Q bottom-up process Event sequence analyzer: NFA q Event detector q k q k+ q acc q rej σ = e(f( ρ ), I). e(f(succ( ρ )), I) ρ = δ (ρ, σ ) nput mage : I Fg. f(succ( ρ )) f( ρ ) - Q succ( ρ ) ρ δ (ρ,.) δ (ρ,.) δ (ρ,.) δ (ρ,.) - Focusng-regon sequence: f(q) Selectve Attenton Mechansm. Y X tme F : F = fq acc ;q rej g ( ) = q ( 6) + = ( ; ) top-down process : ( ) NFA q f(q) : t I(t) a(t) f ( <<) e(f;i(t)) e(f;i(t)) = ( ; f = or jf\a(t)j jf j > ; otherwse () j j `'

4 '96/xx Vol. J79{D{II No. xx = q k ( ). Table State transton table at = q k for event code length=. e(f(q k );I ) e(f(q k+ );I ) + q rej q k+ q k fq k ;q k+ g 6 f ; ; ; g NFA q acc Intalzaton: =; = q Step: = e(f( );I(t ))e(f (succ( ));I(t )) Step: + = ( ; ) Step3: = +, goto Step = q k + = fq k ;q k+ g Step 3 f(q ) f(q ) q q )q ) 3) (q rej ).. succ() = q q + ) ) q acc [ ] C C 86 ( (; ) j= q rej ) ( C ) (; ) C) () _((; ) C ) C) ): P C k C k z k ID C k j= C j ) z k j= z j [ ] ID ID ID ID

5 t t + Tme C token C C + C + C 3 C C + C + 3 ID. Fg. 3 Token ID propagaton va lnks. actve state nactve state propageted tokens generated token t t + C j Ck + C j Ck + C j Ck + [ 6 [ C j (; ) A \ C k + j= ; (3) C j Ck + ID ( ) ID ( 3) 3 C,C,C 3 C j C j ID 3 C+ t + ID 3 C 4 C j C j ID t + ID ID (4) ID 3 C j 4 ID C k + ID q ε ε ε ε Event sequence analyzer qω q k ω q k+ ω acc qω qω rej Event sequence analyzer qω q k ω q k+ ω q acc ω qω rej Event sequence analyzer qωn q k ωn q k+ ωn 4 Fg. 4 acc qωn qωn rej Behavor classer. Event detector σ ω e(f( ω.ρ ),I) ρ e(f(suc( ρω )),I) ω Event detector σω e(f( ω ),I).ρ e(f(suc( ρω )),I) ρω Event detector σωn e(f( ρ ωn),i). ρ e(f(suc( ρωn )),I) ωn C.! ( =; ;N) q q q! ( 4). 3 NFA n a (t) ( =; ;n) a(t) a(t) Z ja(t) \ a ( (t))j ja(t) [ a ( dt; (4) (t))j

6 '96/xx Vol. J79{D{II No. xx a (t) a j (t) = Standard Sample: a(t) Behavor Object a N (t) Object tme f(t) = N = a( τ (t)) 5 Fg. 5 Learnng a common anomalous regon sequence. f(t) = N = a( τ (t)) t s t e t s t e Tme Slce = Tme Slce = Fg. 6 f( ) = te t = ts f(t) f( ) = te t = ts f(t) 6 Learnng a focusng regon sequence. tme j j f (t) ( 5) f(t) = n\ = a ( (t)): (5) NFA ( 6) NFA q t s < = t<t e f(q) f(q) = t\ e t=t s f(t) (6) ) ) j =,qj = t jf(qj) \ f(qj + )j=jf(qj) [ f (qj + )j Fg. 7 & Image Image 7. Eectveness of mult-vewpont mages. f(q k j+) = 8 >< >: f (q k j ); k< f (qj k ) \ f(qj k+ ); k = f (qj k+ ); k> (7) f(qj+) k =, j = j 3. (N c ) ( ) 3 ( 7)

7 pasted Image Event Detector () Image-level ntegraton Sequence Analyser :Feasble State Combnaton : actve state : Inhbted actve state Event Detector Event Detector Integrated Event Code Sequence Analyser 4 5 :Feasble Path 9 (N c =) Fg. 9 State product space(n c =). () Event-level Integraton Common Anomalous Regon Sequence- Fg. 8 Event Detector Sequence Analyser Event Detector Sequence Analyser (3) State-level Integraton 8 Three types of nformaton ntegraton. Inhbton Common Anomalous Regon Sequence- Fg. Learnng a feasble path. tme ) ) 3) ( 8) 3. ( ) 7 3. c(c =; ;N c ) I c e c e all NFA e all e all = \e c N c 3. 3 ( ) c(c = ; ;N c) Q c = fqc; ;q m c c g 9 Q Q N c ID

'96/xx Vol. J79{D{II No. xx 3. 3. ( 9 ) ID 3.

8 '96/xx Vol. J79{D{II No. xx ( 9 ) ID ( [ / ]) (4 ) camera (a) `enter' (a) `ext'! tme! tme (, ) Fg. Examples of tranng data (gray-levelmage, anomalous regon). (

) 6 ( ) ( ) 3 q acc q q acc camera camera!

(a) (b) (c) (d) (e) (e) 3 ( ) t t 4 6 (a)

: 45(75%) enter + ext 4 enter + ext 3 ext x

9 (a) `enter'! tme (a) `ext'! tme Fg. Example of focusng regons. ) 6 ( ) ( ) 3 q acc q q acc camera camera! tme 3 ( ) Fg. 3 An example of test data(extracted). enter camera q acc q acc q q ext camera t t q enter camera ext camera 4 3 (, ) Fg. 4 State Transtons for Fg. 3 (black: actvated states,gray: nhbted states). (a) (b) (c) (d) (e) (e) 3 ( ) t t 4 6 (a) (e) Max : 5(85%) 5 Max : 45(75%) enter + ext 4 enter + ext 3 ext x ext x enter x enter x (a) camera (b) camera Non-Integraton 6 Max : 6(%) 5 enter + ext Max : 4(67%) 4 enter + ext ext x enter x enter x ext x enter + ext Max :59(98%) enter x ext x (c) Image-level Integraton (d) Event-level Integraton (e) State-level Integraton 5 ( :, : ) Fg. 5 Recognton results (Vertcal:Number of correct recognton,horzontal: threshold ). 5.

10 '96/xx Vol. J79{D{II No. xx NFA NFA ( :JSPS-RFTF96P5) (A)()848 [] Yamato J., Ohya J., and Ish K., \Recognzng human acton n tme-sequentalmages usng hdden markov model", Proc. of CVPR, pp , (99) [] Starner T. and Pentland A., \Real-tme Amercan sgn language recognton from vdeo usng hdden markov models", Proc. of ISCV, pp. 65-7, 995. [3] Bregler C. and Omohundro S.M., \Nonlnear manfold learnng for vsual speech recognton", Proc. of ICCV, pp , 995. [4] Wlson A. and Bobck A., \Learng VsualBehavor for Gesture Analyss", M.I.T. Meda Laboratory PerceptualComputng Secton TechncalReport No Davd Marr... Davd Marr IAPR A StructuralAnalyss of Complex Aeral Photographs (PLENUM), SIGMA: A Knowledge-Based Aeral Image Understandng System (PLENUM),

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