Abstract
An original approach for real time detection of changes in motion is presented, for detecting and recognizing events. Current video change detection focuses on shot changes, based on appearance, not motion. Changes in motion are detected in pixels that are found to be active, and this motion is input to sequential change detection, which detects changes in real time. Statistical modeling of the motion data shows that the Laplace provides the most accurate fit. This leads to reliable detection of changes in motion for videos where shot change detection is shown to fail. Once a change is detected, the event is recognized based on motion statistics, size, density of active pixels. Experiments show that the proposed method finds meaningful changes, and reliable recognition.
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References
Chavez, G.C., Cord, M., Philip-Foliguet, S., Precioso, F., de Araujo, A.: Robust scene cut detection by supervised learning. In: EUPISCO (2006)
Ajay, D., Radhakrishan, R., Peker, K.: Video summarization using descriptors of motion activity: a motion activity based approach to key-frame extraction from video shots. J. Electronic Imaging 10, 909–916 (2001)
Aach, T., Kaup, A., Mester, R.: Statistical model-based change detection in moving video. Signal Processing 31, 165–180 (1993)
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 780–785 (1997)
Hassouni, M., Cherifi, H., Aboutajdine, D.: Hos-based image sequence noise removal. IEEE Transactions on Image Processing 15, 572–581 (2006)
Giannakis, G., Tsatsanis, M.K.: Time-domain tests for Gaussianity and time-reversibility. IEEE Transactions on Signal Processing 42, 3460–3472 (1994)
Briassouli, A., Kompatsiaris, I.: Robust temporal activity templates using higher order statistics. IEEE Transactions on Image Processing (to appear)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1999 (1999)
Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27, 773–780 (2006)
Dragalin, V.P.: Optimality of a generalized cusum procedure in quickest detection problem. In: Statistics and Control of Random Processes: Proceedings of the Steklov Institute of Mathematics, pp. 107–120. Providence, Rhode Island (1994)
Moustakides, G.V.: Optimal stopping times for detecting changes in distributions. Ann. Statist. 14, 1379–1387 (1986)
Page, E.S.: Continuous inspection scheme. Biometrika 41, 100–115 (1954)
Muthukrishnan, S., van den Berg, E., Wu, Y.: Sequential change detection on data streams. In: ICDM Workshop on Data Stream Mining and Management, Omaha NE (2007)
Lelescu, D., Schonfeld, D.: Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream. IEEE Transactions on Image Processing 5, 106–117 (2003)
Bansal, R.K., Papantoni-Kazakos, P.: An algorithm for detecting a change in a stochastic process. IEEE Transactions on Information Theory 32, 227–235 (1986)
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Avgerinakis, K., Briassouli, A., Kompatsiaris, I. (2011). Real Time Motion Changes for New Event Detection and Recognition. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6468. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22822-3_6
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DOI: https://doi.org/10.1007/978-3-642-22822-3_6
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