Spatio-temporal segmentation and regions tracking of high definition video sequences based on a Markov Random Field model
In this paper, we propose a Markov random field sequence segmentation and regions tracking model, which aims at combining color, texture, and motion features. First a motion-based segmentation is realized. Namely the global motion of the video sequence is estimated and compensated. From the remaining motion information, a rough motion segmentation is achieved. Then, we use a Markovian approach to update and track over time the video objects. The spatio-temporal map is updated and compensated using our Markov Random Field segmentation model to keep consistency in video objects tracking.