Mid-level features and spatio-temporal context for activity recognition
نویسندگان
چکیده
منابع مشابه
Mid-level features and spatio-temporal context for activity recognition
Local spatio-temporal features have been shown to be effective and robust in order to represent simple actions. However, for high level human activities with long-range motion or multiple interactive body parts and persons, the limitation of low-level features blows up because of their localness. This paper addresses the problem by suggesting a framework that computes mid-level features and tak...
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Local spatio-temporal features have been shown to be efficient and robust to represent simple actions. However, for complicated human activities with long-range motion or multiple interactive body parts and persons, the limitation of low-level features blows up because of their local properties and the lack of context. This paper addresses the problem by suggesting a framework for both computin...
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Introduction Recognition of human activity from video data is a challenging problem that has received an increasing amount of attention from the computer vision community in recent years. The ability to parse high-level visual information has wide-ranging applications that include surveillance and security, the aid of people with special needs and the understanding of human non-verbal communica...
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The local feature based approaches have become popular for activity recognition. A local feature captures the local movement and appearance of a local region in a video, and thus can be ambiguous; e.g., it cannot tell whether a movement is from a person’s hand or foot, when the camera is far away from the person. To better distinguish different types of activities, people have proposed using th...
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2012
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2012.05.001