Recognition of dynamic video contents based on motion texture statistical models

Abstract

The aim of this work is to model, learn and recognize, dynamic contents in video sequences, displayed mostly by natural scene elements, such as rivers, smoke, moving foliage, fire, etc. We adopt the mixed-state Markov random fields modeling recently introduced to represent the so-called motion textures. The approach consists in describing the spatial distribution of some motion measurements which exhibit values of two types: a discrete component related to the absence of motion and a continuous part for measurements different from zero. Based on this, we present a method for recognition and classification of real motion textures using the generative statistical models that can be learned for each motion texture class. Experiments on sequences from the DynTex dynamic texture database demonstrate the performance of this novel approach.