Optimizing Superpixel clustering for real-time egocentric-vision applications

In this work, we propose a strategy for optimizing a superpixel algorithm for video signals, in order to get closer to real time performances which are on the one hand needed for egocentric vision applications and on the other must be bearable by wearable technologies. Instead of applying the algorithm frame by frame, we propose a technique inspired to Bayesian filtering and to video coding which allows to re-initialize superpixels using the information from the previous frame. This results in faster convergence and demonstrates how performances improve with respect to the standard application of the algorithm from scratch at each frame.