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Human movements are important cues for recognizing human actions, which can be captured by explicit modeling and tracking of actor or through space-time low-level features. However, relying solely on human dynamics is not enough to discriminate between actions which have similar human dynamics, such as smoking and drinking, irrespective of the modeling method. Object perception plays an important...
We present an approach to recognizing single actor human actions in complex backgrounds. We adopt a Joint Tracking and Recognition approach, which track the actor pose by sampling from 3D action models. Most existing such approaches require large training data or MoCAP to handle multiple viewpoints, and often rely on clean actor silhouettes. The action models in our approach are obtained by annotating...
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