RFSOM - Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation

In this work we propose an online approach to compute a more precise assignment between parts of an upper human body model to RGBD image data. For this, a Self-Organizing Map (SOM) will be computed using a set of features where each feature is weighted by a relevance factor (RFSOM). These factors are computed using the generalized matrix learning vector quantization (GMLVQ) and allow to scale the input dimensions according to their relevance. With this scaling it is possible to distinguish between the different body parts of the upper body model. This method leads to a more precise positioning of the SOM in the 2.5D point cloud, a more stable behavior of the single neurons in their specific body region, and hence, to a more reliable pose model for further computation. The algorithm was evaluated on different data sets and compared to a Self-Organizing Map trained with the spatial dimensions only using the same data sets.