Journal
INTERNATIONAL JOURNAL OF COMPUTER VISION
Volume 107, Issue 2, Pages 191-202Publisher
SPRINGER
DOI: 10.1007/s11263-013-0683-3
Keywords
Early detection; Event detection; Structured output learning
Categories
Funding
- National Science Foundation (NSF) [RI-1116583]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1116583] Funding Source: National Science Foundation
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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.
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