Temporal segmentation and assignment of successive actions in a long-term video |
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Authors: | Guoliang Lu Mineichi Kudo Jun Toyama |
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Affiliation: | Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan |
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Abstract: | Temporal segmentation of successive actions in a long-term video sequence has been a long-standing problem in computer vision. In this paper, we exploit a novel learning-based framework. Given a video sequence, only a few characteristic frames are selected by the proposed selection algorithm, and then the likelihood to trained models is calculated in a pair-wise way, and finally segmentation is obtained as the optimal model sequence to realize the maximum likelihood. The average accuracy on IXMAS dataset reached to 80.5% at frame level, using only 16.5% of all frames in computation time of 1.57 s per video which has 1160 frames on the average. |
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Keywords: | Action segmentation Characteristic frames Viterbi algorithm |
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