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Detecting and Naming Actors in Movies using Generative Appearance Models

Vineet Gandhi 1, * Rémi Ronfard 1, * 
* Corresponding author
1 IMAGINE - Intuitive Modeling and Animation for Interactive Graphics & Narrative Environments
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We introduce a generative model for learning person and costume specific detectors from labeled examples. We demonstrate the model on the task of localizing and naming actors in long video sequences. More specifically, the actor's head and shoulders are each represented as a constellation of optional color regions. Detection can proceed despite changes in view-point and partial occlusions. We explain how to learn the models from a small number of labeled keyframes or video tracks, and how to detect novel appearances of the actors in a maximum likelihood framework. We present results on a challenging movie example, with 81% recall in actor detection (coverage) and 89% precision in actor identification (naming).
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Submitted on : Thursday, August 29, 2013 - 1:37:05 PM
Last modification on : Thursday, January 20, 2022 - 5:30:20 PM
Long-term archiving on: : Monday, December 2, 2013 - 8:52:55 AM


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Vineet Gandhi, Rémi Ronfard. Detecting and Naming Actors in Movies using Generative Appearance Models. CVPR 2013 - International Conference on Computer Vision and Pattern Recognition, IEEE, Jun 2013, Portland, Oregon, United States. pp.3706-3713, ⟨10.1109/CVPR.2013.475⟩. ⟨hal-00814197⟩



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