Compressed-Domain Video Object Tracking Using Markov Random Fields with Graph Cuts Optimization

Abstract

We propose a method for tracking objects in H.264/AVC compressed videos using a Markov Random Field model. Given an initial segmentation of the target object in the first frame, our algorithm applies a graph-cuts-based optimization to output a binary segmentation map for the next frame. Our model uses only the motion vectors and block coding modes from the compressed bitstream. Thus, complexity and storage requirements are significantly reduced compared to pixel-domain algorithms. We evaluate our method over two datasets and compare its performance to a state-of-the-art compressed-domain algorithm. Results show that we achieve better results in more challenging sequences.