TAILIEUCHUNG - Max-Margin Early Event Detectors

Obviously you will need to tailor your walk event to the interests and abilities of the people you plan to attract. This will affect your choice of route, its length and difficulty and the way you publicise and promote it. It is often a good idea to offer a shorter route option for people who do not want to walk the whole way. This can help widen the appeal of your event to young families and older people. Think about what you need your walkers to know and when they need to know it. The following list. | Max-Margin Early Event Detectors Minh Hoai Fernando De la Torre Robotics Institute Carnegie Mellon University Abstract 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. 1. Introduction The ability to make reliable early detection of temporal events has many potential applications in a wide range of fields ranging from security . pandemic attack detection environmental science . tsunami warning to healthcare . risk-of-falling detection and robotics . affective computing . A temporal event has a duration and by early detection we mean to detect the event as soon as possible after it starts but before it ends as illustrated in Fig. 1. To see why it is important to detect events before they finish consider a concrete example of building a robot that can affectively interact with humans. Arguably a key requirement for such a robot is its ability to accurately and rapidly detect the human emotional states from facial expression so that appropriate responses can be made in a timely manner. More often than not a socially acceptable response is to imitate the current human behavior. This requires facial events such as smiling or frowning to be detected even before they are complete otherwise the imitation .

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