Label propagation in videos indoors with an incremental non-parametric model update

Semantic interpretation of the environment can significantly improve the capabilities of our autonomous robots. This work is focused on automatic semantic label propagation in video of indoor environments acquired by a mobile robot. Using a small number of training examples, we propose a new approach to recognize and label dominant background regions of interest, such as floor, wall and doors, and separate them from the remaining of foreground/object image categories. Our approach performs the labeling at the level of image superpixels. A simple non-parametric model is initialized from a few hand labeled examples in the first frame, and then it is propagated and updated along the sequence. We demonstrate the promising results obtained with our proposal in five different indoor sequences from different environments. The obtained semantic labeling can be used both for autonomous navigation and to provide better context for subsequent object detection.