Abstract
Detecting and locating a desired information in hefty amount of video data through manual procedure is very cumbersome. This necessitates segregation of large video into shots and finding the boundary between the shots. But shot boundary detection problem is unable to achieve satisfactory performance for video sequences consisting of flash light and complex object/camera motion. The proposed method is intended for recognising abrupt boundary between shots in the presence of motion and illumination change in an automatic way. Typically any scene change detection algorithm assimilates time separation in a shot resemblance metric. In this communication, absolute sum gradient orientation feature difference is matched to automatically generated threshold for sensing a cut. Experimental study on TRECVid 2001 data set and other publicly available data set certifies the potentiality of the proposed scheme that identifies scene boundaries efficiently, in a complex environment while preserving a good trade-off between recall and precision measure.




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Sharma, R.A., Gandhi, V., Chari, V., Jawahar, C.V.: Automatic analysis of broadcast football videos using contextual priors. Signal Image Video Process. (SIViP) 11(1), 171–178 (2017)
Cotsaces, C., Nikolaidis, N., Pitas, I.: Video shot boundary detection and condensed representation: a review. IEEE Signal Process. Mag. 23(2), 28–37 (2006)
Koprinska, I., Carrato, S.: Temporal video segmentation: a survey. Signal Proc. Image Commun. 16, 477–500 (2001)
Hanjalic, A.: Shot boundary detection: unravelled and resolved. IEEE Trans. Circuits Syst. Video Technol. 12(2), 90–105 (2002)
Boreczky, J.S., Rowe, L.A.: Comparison of video shot boundary detection techniques. In: Proceedings of the SPIE Conference on Storage and Retrieval for still image and Video Databases 2670(IV), pp. 170–179 (1996)
Zhang, H., Kankanhalli, A., Smoliar, S.: Automatic partitioning of full motion video. Multimed. Syst. 1, 10–28 (1993)
Lienhart, R.: Comparison of automatic shot boundary detection algorithms. Proc. SPIE Image Video Process. 3656(VII), 25–30 (1999)
Tasdemir, K., Cetin, A.E.: Motion vector based feature for content based video copy Detection. In: 20th International Conference on Pattern Recognition (ICPR), pp. 3134–3137 (2010)
Tasdemir, K., Cetin, A.E.: Content-based video copy detection based on motion vectors estimated using a lower frame rate. Signal Image Video Process. (SIViP) 8(6), 1049–1057 (2014)
Nagasaka, A., Tanka, Y.: Automatic video indexing and full video search for object appearance. In: Proceedings of the Second Working Conference on Visual Database Systems, vol. II, pp. 113–127 (1991)
Zabih, R., Miller, J., Mai, K.: A feature based algorithm for detecting and classifying scene breaks. In: Proceedings of the ACM Multimedia, vol. 95, pp. 189–200. San Francisco CA (1995)
Yoo, H.W., Ryoo, H.J., Jang, D.S.: Gradual shot boundary detection using localised edge blocks. Multimed. Tools Appl. 28, 283–300 (2006)
Lian, S.: Automatic video temporal segmentation based on multiple features. Soft Comput. 15(3), 469–482 (2011)
Lakshmipriya, G.G., Domnic, S.: Walsh–hadamard transform kernel-based feature vector for shot boundary detection. IEEE Trans. Image Process. 23(12), 5187–5197 (2014)
Cheol, K., Cheon, Y., Kim, G., Choi, H.: Robust scene change detection algorithm for flashlights. In: Proceedings of the International Conference on Computational Science and its Applications (ICCSA), Kuala Lumpur, Malaysia, pp. 26–29, 1003–1013 (2007)
Warhade, K.K., Merchant, S.N., Desai, U.B.: Shot boundary detection in the presence of fire flicker and explosion using stationary wavelet transform. Signal Image Video Process. (SIViP) 5(4), 507–515 (2011)
Warhade, K.K., Merchant, S.N., Desai, U.B.: Shot boundary detection in the presence of illumination and motion. Signal Image Video Process. (SIViP) 7(3), 581–592 (2013)
Vila, M., Bardera, A., Xu, Q., Feixas, M., Sbert, M.: Tsallis entropy-based information measures for shot boundary detection and key frame selection. Signal Image Video Process. (SIViP) 7(3), 507–520 (2013)
Gao, Z., Lu, G., Yan, P., Wang, L.: Retrospective analysis of time series for frame selection in surveillance video summarization. Signal Image Video Process. 1–8 (2016). doi:10.1007/s11760-016-0997-z
Kar, T., Kanungo, P.: Cut detection using block based center symmetric local binary pattern. In: International Conference on Man and Machine Interfacing (MAMI) (2015)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, vol. 3/e. Pearson Education, Upper Saddle River, NJ (2008)
Stockham, T.G.: Image processing in the context of a visual model. Proc. IEEE 60(7), 828–842 (1972)
TRCVID Dataset available on website. www.open-video.org
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Kar, T., Kanungo, P. A motion and illumination resilient framework for automatic shot boundary detection. SIViP 11, 1237–1244 (2017). https://doi.org/10.1007/s11760-017-1080-0
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DOI: https://doi.org/10.1007/s11760-017-1080-0