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Unsupervised Discovery, Modeling and Analysis of long term Activities

Abstract : This work proposes a complete framework for human activity discovery, modeling, and recognition using videos. The framework uses trajectory information as input and goes up to video interpretation. The work reduces the gap between low-level vision information and semantic interpretation, by building an intermediate layer composed of Primitive Events. The proposed representation for primitive events aims at capturing meaningful motions (actions) over the scene with the advantage of being learned in an unsupervised manner. We propose the use of Primitive Events as descriptors to discover, model, and recognize activities automatically. The activity discovery is performed using only real tracking data. Semantics are added to the discovered activities (e.g., "Preparing Meal", "Eating") and the recognition of activities is performed with new datasets.
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https://hal.inria.fr/inria-00621201
Contributor : Guido Pusiol <>
Submitted on : Friday, September 9, 2011 - 4:18:31 PM
Last modification on : Thursday, February 11, 2021 - 2:56:34 PM
Long-term archiving on: : Monday, December 5, 2016 - 12:26:49 AM

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Guido Pusiol, François Bremond, Monique Thonnat. Unsupervised Discovery, Modeling and Analysis of long term Activities. international Conference in Vision Systems, Nov 2011, Sophia Antipolis, France. ⟨inria-00621201⟩

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