A New Framework for Automatic Feature Selection for Tracking

A new framework of recurrent neural network is proposed in this paper for automatic feature selection for tracking. The network is not designed particularly for conventional applications such as pattern classification, association, and recognition; instead, it captures parts of those ingredients for identification of unique features from given sets of data. The architecture extracts different types of textures defined by natural importance to the datasets. These textural layers are then fused into single layer feature where the neurons compete and converge with few iterations based on the criteria of uniqueness of the textually maximized features. The automatically selected features by winning neurons, if any, are determined once and applied for subsequent feature tracking within the same architecture. Experiments performed on video sequence showed that the framework for feature selection and tracking is acceptable to gradual in-plane rotation and some degree of scale and out-of-plane rotation.