Abstract
AbstractThe recognition of transitive, goal-directed actions requires a sensible balance between the representation of specific shape details of effector and goal object and robustness with respect to image transformations. We present a biologically-inspired architecture for the recognition of transitive actions from video sequences that integrates an appearance-based recognition approach with a simple neural mechanism for the representation of the effector-object relationship. A large degree of position invariance is obtained by nonlinear pooling in combination with an explicit representation of the relative positions of object and effector using neural population codes. The approach was tested on real videos, demonstrating successful invariant recognition of grip types on unsegmented video sequences. In addition, the algorithm reproduces and predicts the behavior of action-selective neurons in parietal and prefrontal cortex.KeywordsAction RecognitionPrecision GripTransitive ActionPower GripPosition InvarianceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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