Abstract
We extend our previous work [1] of the general framework for video data mining to further address the issue such as how to mine video data, in other words, how to extract previously unknown knowledge and detect interesting patterns. In our previous work, we have developed how to segment the incoming raw video stream into meaningful pieces, and how to extract and represent some feature (i.e., motion) for characterizing the segmented pieces. We extend this work as follows. To extract motions, we use an accumulation of quantized pixel differences among all frames in a video segment. As a result, the accumulated motions of segment are represented as a two dimensional matrix. We can get very accurate amount of motion in a segment using this matrix. Further, we develop how to capture the location of motions occurring in a segment using the same matrix generated for the calculation of the amount. We study how to cluster those segmented pieces using the features (the amount and the location of motions) we extract by the matrix above. We investigate an algorithm to find whether a segment has normal or abnormal events by clustering and modeling normal events, which occur mostly. In addition to deciding normal or abnormal, the algorithm computes Degree of Abnormality of a segment, which represents to what extent a segment is distant to the existing segments in relation with normal events. Our experimental studies indicate that the proposed techniques are promising.
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Oh, J., Lee, J., Kote, S., Bandi, B. (2003). Multimedia Data Mining Framework for Raw Video Sequences. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds) Mining Multimedia and Complex Data. PAKDD 2002. Lecture Notes in Computer Science(), vol 2797. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39666-6_2
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DOI: https://doi.org/10.1007/978-3-540-39666-6_2
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