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Max-margin early event detectors

7 years 6 months ago
Max-margin early event detectors
The need for early detection of temporal events from sequential data arises in a wide spectrum of applications ranging from human-robot interaction to video security. While temporal event detection has been extensively studied, early detection is a relatively unexplored problem. This paper proposes a maximum-margin framework for training temporal event detectors to recognize partial events, enabling early detection. Our method is based on Structured Output SVM, but extends it to accommodate sequential data. Experiments on datasets of varying complexity, for detecting facial expressions, hand gestures, and human activities, demonstrate the benefits of our approach. To the best of our knowledge, this is the first paper in the literature of computer vision that proposes a learning formulation for early event detection.
Minh Hoai Nguyen, Fernando De la Torre
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Minh Hoai Nguyen, Fernando De la Torre
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