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Most existing approaches for learning action models work by extracting suitable low-level features and then training appropriate classifiers. Such approaches require large amounts of training data and do not generalize well to variations in viewpoint, scale and across datasets. Some work has been done recently to learn multi-view action models from Mocap data, but obtaining such data is time consuming...
Graphical models have been shown to provide a natural framework for modelling high level action transition constraints, and to simultaneously segment and recognize a sequence of actions. Spatio-temporal interest points (STIPs) have been proposed as suitable features for action detection. These interest points are typically mapped to a set of codewords, and actions are detected by accumulating the...
Actions in real world applications typically take place in cluttered environments with large variations in the orientation and scale of the actor. We present an approach to simultaneously track and recognize known actions that is robust to such variations, starting from a person detection in the standing pose. In our approach we first render synthetic poses from multiple viewpoints using Mocap data...
We present a top-down approach to simultaneously track and recognize articulated full-body human motion using learned action models that is robust to variations in style, lighting, background,occlusion and viewpoint. To this end, we introduce the hierarchical variable transition hidden Markov model (HVT-HMM) that is a three-layered extension of the variable transition hidden Markov model (VTHMM)....
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