Video Shot Boundary Detection Algorithm

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1 Vdeo Shot Boundary Detecton Algorthm Kyong-Cheol Ko 1, Young- Mn Cheon 1, Gye-Young Km 1, Hyung Il Cho 1, Seong-Yoon Shn, and Yang-Won Rhee 1, * Informaton Meda Technoy Research Insttute, Soongsl Unversty 1-1, Sangdo-Dong, Dongak-Gu, Seoul , South Korea {hc, gykm11}@ssu.ac.kr Department of Computer Informaton Scence, Kunsan Natonal Unversty 68, Mryong-dong, Kunsan, Chonbuk , South Korea {roadkkc, ywrhee}@kunsan.ac.kr Abstract. We present a newly developed algorthm for automatcally segmentng vdeos nto basc shot unts. A basc shot unt can be understood as an unbroken sequence of frames taken from one camera. At frst we calculate the frame dfference by usng the local hstogram comparson, and then we dynamcally scale the frame dfference by Log-formula to compress and enhance the frame dfference. Fnally we detect the shot boundares by the newly proposed shot boundary detecton algorthm whch t s more robust to camera or obect moton, and many flashlght events. The proposed algorthms are tested on the varous vdeo types and expermental results show that the proposed algorthm are effectve and relably detects shot boundares. 1 Introducton There are many shot detecton methods already proposed n past decades [1], []. The common way for shot detecton s to evaluate the dfference value between consecutve frames represented by a gven feature. Although reasonable accuracy can be acheved, there are stll problem that lmt the robustness of these algorthms [4]. One of the common problems n robust shot detecton results from the fact there are many flashlghts n news vdeo, whch often ntroduce false detecton of shot boundares. Only some smple solutons to ths problem have been proposed n [], [3]. There are man lmtatons are that they assume the flashlghts ust occur durng one frame or lmted wndow regon. In real world, such as news vdeo, there are many flashlght events occur durng a perod of tme and nfluence multple consecutve frames. Another problem that has not been solved very effectvely well s threshold selecton when comparson changes between two frames. Most of the exstng methods use global pre-defned thresholds, or smple local wndow based adaptve threshold. Global threshold s defntely not effcent snce the vdeo property could change dramatcally when content changes, and t s often mpossble to fnd a unversal optmal threshold method also has ts lmtaton because n some stuaton the local statstcs are polluted by strong noses such as bg motons or flashlghts. * Ths work was supported by the Korea research Foundaton Grant (KRF J P. Kalra and S. Peleg (Eds.: ICVGIP 006, LNCS 4338, pp , 006. Sprnger-Verlag Berln Hedelberg 006

2 Vdeo Shot Boundary Detecton Algorthm 389 The obectve of ths paper are: 1 to provde the metrcs that are robust to camera and obect moton, and enough spatal nformaton s retaned, to provde the scaled frame dfference that are dynamcally compressed by formula and t s more convenent to decde the threshold, 3 to propose a new shot boundary detecton algorthm that are robust to camera operaton or fast obect movement, flashlght events. The rest of ths paper s organzed as follows. In the next secton, we provde a proposed algorthm that gves a detal descrpton of the three new algorthms. Secton 3 presents expermental results, and we conclude ths paper and dscuss the future work n Secton 4. The Proposed Algorthm Frstly, we denote the metrcs to extract the frame dfference from consecutve frames. And we scale the frame dfference by formula whch makes more dynamcally robust to any camera or obect moton, and many flashlght events. Fnally we propose the new shot boundary detecton algorthm. Our proposed algorthm works n real tme vdeo stream and not senstve to varous vdeo types. Throughout ths paper, we shall treat a shot, defned as a contnuous sequence of frames recorded from a sngle camera, as a fundamental unt n a vdeo sequence..1 Metrcs n Shot Detecton To segment the vdeo nto shot unts, we should frst defne sutable metrcs to extract frame dfference; so that a shot boundary s declared whenever that metrc exceed a gven threshold. We use the local hstogram comparson that are more robust to camera and obect moton, and enough spatal nformaton s retaned to produce more accurate results [5], [6]. The local hstogram comparson metrcs are defned as: m d( f, f = d ( f, f, bl (1 bl= 1 x d x ( f, f N 1 ( ( (, = H k H k bl ( α r = 1 max( H ( k, H ( k k g ( H ( k H ( k + β g g max( H ( k, H ( k b ( H ( k H ( k + γ, b max( H ( k, H ( k r g b b r r ( where m s the total number of the blocks, and H r (k denotes the hstogram dfference at gray level k for the block bl of th frame n red channels. α, β and γ are

3 390 K.-C. Ko et al. constants and, accordng to NTSC standard, we set these constants to 0.99, 0.587, and 0.114, respectvely. The best frame dfference can be obtaned by breakng the frame nto 16 equal szed regons, usng weghted x -test on color hstograms for these regons and dscardng the largest dfferences to reduce the effects of nose, obect and camera movements.. Scaled Frame Dfference Most of vdeo segmentaton algorthms rely on sutable threshold of smlartes between consecutve frames. However, the thresholds are hghly senstve to the type of nput vdeo. Ths drawback can be overcome by the scaled frame dfference. The scale of frame dfference s performed by Log-formula whch makes more dynamcally compressed frame dfference and Log-formula was referenced by dgtal mage processng whch was used to mage enhancement. The proposed Log formula defned as: d = c (1 + d max( d c = max((1 + d (3 Where d s the frame dfference extracted from equaton (1 and c s the constant calculated from d. Fgure 1 shows the dstrbuton of total frame dfferences extracted from news vdeo. ˆ Œ G œ Œ ˆ Œ G œ Œ d d ˆ ŒG Œ Œ ŠŒGO P ˆ ŒG Œ Œ ŠŒGO Ž P Fg. 1. Dstrbuton of all frame dfference d and d Dstrbuton of all frame dfferences d has wdely spread dfference values n a scaled regon than d and each dfference values are enhanced and concatenated each other more closely. So f we apply the smple shot cut rules, we can detect the shot boundares only usng the frame dfference.

4 Vdeo Shot Boundary Detecton Algorthm 391 Table 1 shows the max (maxmum, mn (mnmum, ave (average, and stdev (standard-devaton represented from three vdeo types(news, sports, adv.. Each of the frame dfferences d and d are calculated from the gven equatons (1 and equaton (. Table 1. Comparson of dfference values d and d vdeos Max. Mn. Ave. Stdev. d d d d d d d d News Sports Adv As mentoned above t, scaled dfference values are more robust and relable to detect the shot boundares and are convenent to select the global threshold. Fgure shows the normal graph of Table 1. Scaled frame dfference d are dynamcally compressed and more normally dstrbuted under the scaled regon than d. Œžš Œžš ˆ U ˆ U š š š š Fg.. Normal Dstrbuton of frame dfference d and scaled frame dfference d.3 Shot Boundary Detecton Algorthm Shot boundary detecton s usually the frst step n generc vdeo processng. A shot represents a sequence of frames captured from a unque and contnuous record from a camera. Therefore adacent frames of the same shot exhbt temporal contnuty. Both the real shot cut and the abrupt cut could cause a great change n frame dfference because of the specal stuatons such as flashlght events, sudden lghtenng varances, and fast camera moton, or large obect movements. So each shot corresponds to a sngle contnuous acton and no change of content can be detected nsde a shot. Change of contents always happen at the boundary between two shots. Parttonng a vdeo sequence nto shots s also useful for vdeo summarzaton and ndexng. We defne shot boundary detecton algorthm based on the temporal property of shot cut and abrupt cut. If the scaled frame dfference of consecutve frames s larger than a max-threshold (thmax, and ts neghborng dfference value of frame dfference s larger than a k-threshold (kgloval, and also ts Eucldan dstance s satsfed wth globalthreshold (thgloval, then the shot cut s detected by shot boundary detecton algorthm. Fgure 3 shows the proposed shot boundary detecton algorthm more detals.

5 39 K.-C. Ko et al. d ( th max fd k global bd k global bfd thglobal Fg. 3. The llustraton graph of proposed shot boundary detecton algorthm As shown n Fgure 3, the shot boundary detecton algorthm can be summarzed as follows: Step 1. At frst, f the scaled frame dfference d ( s larger than a max-threshold th max then the current frame s selected to canddate shot frame, d ( th Step. And we calculate the newly defned dfference value bd (, fd ( as follows: max bd = d ( d ( 1, fd ( ( = d ( + 1 d ( (4 The calculated dfference value bd (, fd ( must be larger than a k-threshold k gloval. bd ( k global && fd ( kglobal Setp 3. Fnally, the Eucldean dstance of each calculated frame dfference value bfd s defned as: bfd + ( ( bd ( ( fd ( = (5

6 Vdeo Shot Boundary Detecton Algorthm 393 And t must larger than a global-threshold th gloval. bfd ( th global Step 1 s the basc step to check the canddate shot frame. Most of shot frame has a bg dfference value and we heurstcally determne the max-threshold th max from scaled frame dfference. In experments results, the determned max-threshold th max was relable and robust than prevous approaches. Step s to check whether the current frame s shot cut or abrupt cut. A real shot cut has enough dstance between bd and fd but abrupt cut has small dstance each other. If the dstance bd and fd s smaller than k-threshold k gloval, then current frame s classfed as abrupt cut. Step 3 s to check the sensblty over the set of threshold bd and fd.. Fgure 4 shows the llustraton of the proposed shot boundary detecton algorthm. ˆ ŒG Œ Œ ŠŒ ˆ ŒG œ Œ X X\_ X`\ Y]\ Z[_ [X\ [Y` Xš Yš Zš [š \š ]š ^š d ( [ZZU^ [Z\U\ [W]U` [Z`U] [[ZU^ [ZWU^ [W`UZ bd ( XY_U\ \YU] ^[U ^ XXZU^ XX_U^ XWWU[ `WUZ fd ( bfd ( [ZZU^ [\YU[ XYXU` XZYU_ ZWU WU_ XYZU^ X]_UW \_U` XZYU] XYYU\ X\_U[ XWWU` XZ\UZ Fg. 4. Dstrbuton of remanng number of frames by the proposed algorthm As shown n Fgure 4, the dagram s the scaled frame dfference of consecutve frames n sequence ntervew vdeos whch has a lot of flashlght events. Detected shot cut frame, and used dfference value of each frame dfference s shown n Fgure 4. All possble shot cut s detected and flashlght s elmnated n relable.

7 394 K.-C. Ko et al. 3 Expermental Results We evaluate the performance of our proposed method wth DrectX 8.1 SDK, MS- Vsual C on Wndows XP. The proposed method has been tested on several vdeo sequences such as news, ntervews, and commercals vdeos that have a lot of scene changes occurs, as shown n table1. Each vdeo sequence has the varous types dgtzed n 30*40 resolutons at 30frames/sec. Table. Descrpton of the Vdeos n the experment dataset # of abrupt cuts Vdeos # of frames moton or etc. fast obect and camera flashlghts # of shot cuts (ground truth news news Choce soccer Flash Move Golf Wne In table, News or Flash1 vdeos contan many flashlghts events and Golf or Wne vdeos contan fast obect and camera motons. We manually dentfy the ground truth by a user wth frame accuracy. In our experments, the shot cut detecton results are compared wth the ground truth n terms of precson and recall. Assume N s the ground truth number of shot cuts, M s the number of mssed cuts and F s the number of false alarms, the recall and precson are defned as follows: N M Re call = N N M Pr ecson = N M + F These two measures are both mportant. We certanly do not want to mss any crtcal shot changes. On the other hand, too many false alarms wll compromse the effcency of vdeo segmentaton. Table 3 ndcates that proposed algorthm can detect not only abrupt cuts but also shot cut wth satsfactory accuracy. Approxmately 97% of fast camera transtons, fast obect motons and flashlght events are detected. The mssed abrupt cuts manly results from the fact that the frame dfferences between consecutve frames are lower than the gven threshold. (3

8 Vdeo Shot Boundary Detecton Algorthm 395 Table 3. Experment Results Vdeos # of abrupt cuts # of shot cuts # of # of false mssed recall precson false mssed recall precson # of # of news1 0 94% 100% 0 100% 93% news 0 100% 97% % 100% Choce % 100% % 100% soccer % 100% % 100% Flash % 100% % 93% Move % 100% 1 8% 90% Golf 0 1 9% 100% % 100% Wne % 100% % 100% TOTAL 6 97% 99% % 97% 4 Concluson Ths paper has presented an effectve shot boundary detecton algorthm, whch focus on three dffcult problems solutons: To provde the metrcs that are robust to camera and obect moton, and enough spatal nformaton s retaned. To provde the scaled frame dfference that are dynamcally compressed by formula and t s more convenent to decde the threshold. To propose a new shot boundary detecton algorthm that are robust to camera operaton or fast obect movement, flashlght events. Experments show that the proposed algorthm s promsng. However the automatc vdeo partton s stll a very challengng research problem especally for detectng gradual transtons or camera fabrcaton, specal events and so on. Further work s stll needed. References 1. I. Koprnska and S. Carrato, Temporal Vdeo Segmentaton: A Survey, Sgnal Processng Image Communcaton, Elsever Scence G. Ananger, T.D.C. Lttle, A survey of technoes for parsng and ndexng dgtal vdeo, Journal of Vsual Communcaton and Image Representaton, 1996, pp D. Zhang, W. Q, H. J. Zhang, A News Shot Boundary Detecton Algorthm, IEEE Pacfc Rm Conference on Multmeda, pp , U. Garg, R. Kastur, and S. H. Strayer, Performance Characterzaton of Vdeo-Shot- Change Detecton Methods, IEEE transacton on crcuts and systems for vdeo technoy, Vol. 10, No. 1, Feb A. Nagasaka, Y. Tanaka, Automatc vdeo ndexng and full-vdeo search for obect appearances, n Vsual Database Systems II, pp , Elsever, K. C. Ko, Y. W. Rhee, Scene Change Detecton usng the Ch-test and Automated Threshold Decson Algorthm, ICCSA06, Vol. 3983, pp , 006.

9 396 K.-C. Ko et al. 7. C. L. Huang and B. Y. Lao, A Robust Scene Change Detecton Method for Vdeo Segmentaton, IEEE Trans on CSVT, Vol. 11. No. 1, pp , December H. Zhang, A. Kankamhall, and S. Smolar, Automatc parttonng of full-moton vdeo, ACM Multmeda Systems, New York: ACM Press, Vol. 1, 1993, pp U. Grag, R. Kastur, S. Antan, Evaluaton of vdeo sequence ndexng and herarchcal vdeo ndexng, n: Proc. SPIE Conf. Storage and Retreval n Image and Vdeo Databases, 1995, pp Gonzalez, Dgtal Image Processng /E, Prentce-Hall, R. M. Ford, C. Robson, D. Temple, M. Gerlach, Metrcs for shot boundary detecton n dgtal vdeo sequences, Multmeda Systems 8: 37-46, A. Ekn, A. M. Tekalp, and R. Mehrotra, Automatc soccer vdeo analyss and summarzaton, IEEE Trans. On Image Processng, Vol. 1, No. 7, pp , July C. L. Huang and B. Y. Lao, A Robust Scene Change Detecton Method for Vdeo Segmentaton, IEEE Trans. Crcut System. Vdeo Technoy, Vol. 11, No. 1, December 001.

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