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Max-Margin Early Event Detectors
by
Hoai, Minh
, De la Torre, Fernando
in
Accuracy
/ Analysis
/ Artificial Intelligence
/ Computer Imaging
/ Computer information security
/ Computer Science
/ Computer vision
/ Detectors
/ Facial
/ Human
/ Image Processing and Computer Vision
/ Pattern Recognition
/ Pattern Recognition and Graphics
/ Recognition
/ Robotics
/ Robots
/ Sensors
/ Sign language
/ Studies
/ Support vector machines
/ Temporal logic
/ Time series
/ Vision
/ Vision systems
2014
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Max-Margin Early Event Detectors
by
Hoai, Minh
, De la Torre, Fernando
in
Accuracy
/ Analysis
/ Artificial Intelligence
/ Computer Imaging
/ Computer information security
/ Computer Science
/ Computer vision
/ Detectors
/ Facial
/ Human
/ Image Processing and Computer Vision
/ Pattern Recognition
/ Pattern Recognition and Graphics
/ Recognition
/ Robotics
/ Robots
/ Sensors
/ Sign language
/ Studies
/ Support vector machines
/ Temporal logic
/ Time series
/ Vision
/ Vision systems
2014
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Do you wish to request the book?
Max-Margin Early Event Detectors
by
Hoai, Minh
, De la Torre, Fernando
in
Accuracy
/ Analysis
/ Artificial Intelligence
/ Computer Imaging
/ Computer information security
/ Computer Science
/ Computer vision
/ Detectors
/ Facial
/ Human
/ Image Processing and Computer Vision
/ Pattern Recognition
/ Pattern Recognition and Graphics
/ Recognition
/ Robotics
/ Robots
/ Sensors
/ Sign language
/ Studies
/ Support vector machines
/ Temporal logic
/ Time series
/ Vision
/ Vision systems
2014
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Journal Article
Max-Margin Early Event Detectors
2014
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Overview
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.
Publisher
Springer US,Springer,Springer Nature B.V
Subject
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