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Klaus Boehm, Wolfgang Broll, Michael Sokolewicz Presented by Ross Byers

Dynamic Gesture Recognition Using Neural Networks; A Fundament for Advanced Interaction Construction. Klaus Boehm, Wolfgang Broll, Michael Sokolewicz Presented by Ross Byers. Interaction in Virtual Spaces. Gestures can provide clear interaction between humans, despite inherent flexibility.

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Klaus Boehm, Wolfgang Broll, Michael Sokolewicz Presented by Ross Byers

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  1. Dynamic Gesture Recognition Using Neural Networks; A Fundament for Advanced Interaction Construction Klaus Boehm, Wolfgang Broll, Michael Sokolewicz Presented by Ross Byers

  2. Interaction in Virtual Spaces • Gestures can provide clear interaction between humans, despite inherent flexibility. • Gestures provide opportunity for use in Virtual Spaces: Interacting with objects, as well as providing instructions • Static gestures (postures) established.

  3. Interaction in Virtual Spaces • Static Gestures are limited. • Requirement to hold pose tiring • Exact manipulation is difficult • No force feedback

  4. Dynamic Gestures • Natural Interaction is movement based, thus dynamic. • Time and movement introduce complications.

  5. Recognition of Dynamic Gestures • The authors used a Kohonen Feature Map (KFM), a type of Neural Network. • Two layers – Input and output • Unsupervised training • Output is a two-dimensional grid of neurons, where spatial proximity on the grid is correlated with similarity.

  6. Preprocessing • Because of high dimensionality (30+ in this example), the data must be preprocessed. • ‘Vertical’ preprocessing collects information for each time step • ‘Horizontal’ preprocessing filters and derives data. • Recording of training data was best assisted by a second person.

  7. First Recognition Approach • Direct Mapping • Finds match for the best gesture • Presents issues with longer and shorter gestures • Introduces lag • Requires differing buffer sizes

  8. Second Recognition Approach • Gesture Parts • Instead of Requiring ‘all at once’ recognition, recognize a library of sub-gestures. • Simplest example would use equidistant time-slices. This does not correctly model real behavior. • KFM used for part recognition only. • Second, specialized NN used for full gesture recognition.

  9. Results • Unfortunately, the authors did not share their accuracy data. • They do however, reflect on processing time. • The first approach required a reduction of input data to run in real time, and was considered ‘suitable’ for 10 gestures. • The second approach allows for more preprocessing, thus improving performance even with second neural network.

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