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As computing devices become more powerful, smaller and more widespread, what’s limiting their potential is their inability to perceive and interact with the environment they are embedded in.
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Of all possible objects in the environment, arguably the single most important object class in the context of making computing devices usable in day-to-day life are humans.
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I am interested in developing computer vision techniques for detecting, tracking and recognizing humans in images, and understanding their activities.
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Current Projects |
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Visual detection and tracking of humans, 2005-2008, Univ. of Amsterdam, Netherlands
The ability to recognize humans and their activities by vision is key for a machine to interact intelligently and effortlessly with a human-inhabited environment. There are numerous important applications ranging from public safety, elderly care and intelligent vehicles to human motion capture/analysis. In this research we investigate generic techniques for person detection and tracking. Challenges are abound: what features to use (2D vs. 3D, model-based vs. model-free), how to efficiently (re)initialize the model, how to adapt a generic model to particular image data, how to deal with (self) occlusion and uncertainty, etc.
Ph.D. Student: Michael Hofmann
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CASSANDRA: Aggression Detection by Fusion Video and Audio, 2005-2009, Univ. of Amsterdam, Netherlands
This project pursues human activity recognition in dynamic environments, in particular, automatic aggression detection. Because events associated with the buildup or enactment of aggression are difficult to detect by a single sensor modality (e.g. shouting versus hitting-someone), CASSANDRA combines audio- and video-sensing. The current project status is described here. See also Looking-at-People
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Past Projects |
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Lectures |
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