1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2
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1 CHLAC ,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for suspicious behavior detection based on CHLAC method. We focus on suspicious behavior that consists of set of actions. Generally such suspicious behavior can t be detected by single action recognition. To deal with this problem, action sequence, which is decomposition of behavior, is target to be recognized. First, we have designed CHLAC subspace for each single action using PCA analysis. Each action subspace is used to detect the action included in input behavior which is consider as sequence of action. Next, we make detection list of input behavior, elements of which are actions and are sorted in time order. Finally, detection list is surveyed from a point view that suspicious action sequence is included in it or not. Through experiment the validity of our proposed method has been shown. 1.,,.,,,.,., CHLAC.,,,.,.,,, CHLAC,.,, CHLAC.,,,, 4,,.,., 4,.,,.,., Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering 2 Kyushu Institute of Technology Information Science Center 3 Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering 1 c 2012 Information Processing Society of Japan
2 1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2 Local 3*3 masks up to the second order x(a 1,..., a N ) = f(r)f(r + a 1)...f(r + a N ) (1),, N a i (i = 0,..., N) , HLAC, 0 1, 1 4, HLAC, 2.,., A f A, B f B, A B 2, f A + f B. 2.4 (CHLAC ) HLAC 2, CHLAC., N. x N f (a 1,..., a N ) = f(r)f(r + a 1)...f(r + a N ) (2),. CHLAC, 0 1, 1 13, 2 237, 251. CHLAC HLAC. 2 c 2012 Information Processing Society of Japan
3 2.5,, CHLAC x i (i = 1,..., N),..,. µ. Σ x = 1 N ( (xi µ)(x i µ) ) T (3) N i=1 Input: have a bag Input: no bag 3 Fig. 3 Detection of bag experiment result. Λ = diag(λ 1,..., λ M ), η K K i=1 λi η K = M λ j=1 j (4), η K 0.99 u 1,..., u K.. U K = [u 1,...u K ] U KUK T. x d 2 = x T x x T U K UKx T (5). d. 2.6 CHLAC ,,, 4., sony handycam HDR-CX700. CHLAC 20, 1.,,,. 3. Input: left walk Input: right walk 4 Fig. 4 Ditection of direction experiment result,.,.,,., 2.,.,.,,,.,, 2. 4.,, 3 c 2012 Information Processing Society of Japan
4 ., CHLAC. 3.. right walk left walk right walk with bag left walk with bag 5 Fig. 5 Detection of motion experiment result,., 2., 2. 4,., 2,.,.,.,. 5., 4 all.,.,., 3.1..,,,. 1, 2..,,,. 3.2,.,,.,., 1 1,.,, 1 1..,..,,, 4., 6.. k Rank 4 c 2012 Information Processing Society of Japan
5 6 Fig. 6 Sequence sorted list. 2, ,,.,. 4.,,.,. 4.1, ,,, 4., 1 4 all.,,,. 8., sony handycam HDR-CX700, CHLAC 20, 1, Fig. 7 Flow of detection of suspicious behavior ,,,, all 5. 9., all., t=400., 9., all 5 c 2012 Information Processing Society of Japan
6 ,. 4.2, ,,, 4., 1 4 all.,,.,,. 10., sony handycam HDR-CX700, CHLAC 20, ,,,,, all all, ,, 2,.,, 2,.,. 5., CHLAC,.,., all,.,,.,,.,.,..,,. 1),. PRMU Vol. 104 No. 291 pp ),,,,, PRMU , Vol.108, No.198, pp , ),, SSII, ),,, CHLAC, MIRU, ),, Vol. 48 No. 1 pp ),, D, J63-D-4, , ) T.Kobayashi and N.Otsu. Action and Simultaneous Multiple-Pewson Identification Using Cubic Higher-Order Local Auto-Correlation, In Proc. International Conference on Pattern Recognition, pp , ),,,, Vol. 122-D, No. 2, pp , c 2012 Information Processing Society of Japan
7 t=0 t=160 t=250 t=350 t=120 t=400 t=390 t=400 t=405 t=425 t=410 t=430 8 Fig. 8 Input of abnormality motion 9 Fig. 9 Result of abnormality detection 7 c 2012 Information Processing Society of Japan
8 t=0 t=50 t=120 t= Fig. 11 Result of suspicious detection t=250 t=300 t=320 t= Fig. 10 Input of suspicious behavior Fig Sequence sorted list of suspicious detection 8 c 2012 Information Processing Society of Japan
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