Water detection with segmentation guided dynamic texture recognition

This paper proposes a model for water detection in video sequences, which is a key asset of any robot operating in natural environments. By searching the visual input for the waters typically chaotic dynamic texture, the model is able to filter out the static background and even any dynamic object present in the scene. In this work, the waters signature is defined, mostly, in terms of an entropy measure computed from the optical flow obtained across several frames. To foster the classification of motionless regions in the visual input, usually associated to the far field, a segmentation guided label propagation method is used. The model is experimentally validated on 12 diverse videos, acquired from static and moving cameras.