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• W2187782563 abstract "Action and event recognition from video require com- paring temporal sequences of images, or of intermedi- ate representations derived from them. Such a compari- son should be insensitive to intrinsic temporal variations within the same class - for instance the speed of execu- tion of a particular gesture - and at the same time retain the discriminative power that enable classifying differ- ent actions. In this paper, we propose a technique to compare temporal sequences that accounts for dynamic constraints implicit in the data generation process. Our technique is more flexible than those previously used for quasi-periodic actions such as walking gaits, but more discriminative than others based on dynamic time warp- ing that do not satisfy dynamic constraints. We illustrate our approach on public datasets including stationary and non-stationary actions, using both motion-capture and image data. In all the experiments we have con- ducted, our approach outperforms competing ones. We highlight experiments where it exhibits limitations." @default.
• W2187782563 created "2016-06-24" @default.
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• W2187782563 date "2007-01-01" @default.
• W2187782563 modified "2023-09-23" @default.
• W2187782563 title "Time Warping Under Dynamic Constraints With Application to Non-Stationary Action Alignment and Classification" @default.
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