Abstract
It is a challenge to quickly and accurately access interesting visual events in explosively increasing surveillance video data. In this paper, we develop a novel video summarization framework to accurately detect and quickly browse interesting visual events in large-scale surveillance videos. The method firstly detects interesting foreground objects by running a set of pre-trained object classifiers on foreground object candidates which are generated by a background subtraction algorithm. Then a Hungarian algorithm based multi-objects tracking program is followed to obtain accurate and complete motion trajectories of detected foreground objects. Finally, each interesting visual event is compactly represented by a synthetical snapshot, which makes it convenient to quickly access interesting visual events in long videos. Experiments on challenging surveillance videos show our framework outperforms existing video summarization systems in the detection accuracy of interesting visual events.
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Yuan, F., Tang, C., Tian, S., Hao, H. (2013). A Framework for Quick and Accurate Access of Interesting Visual Events in Surveillance Videos. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_17
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DOI: https://doi.org/10.1007/978-3-642-41939-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41938-6
Online ISBN: 978-3-642-41939-3
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