Space-Time Joint Multi-Layer Segmentation and Depth Estimation

Video-based segmentation and reconstruction techniques are predominantly extensions of techniques developed for the image domain treating each frame independently. These approaches ignore the temporal information contained in input videos which can lead to incoherent results. We propose a framework for joint segmentation and reconstruction which explicitly enforces temporal consistency by formulating the problem as an energy minimisation generalised to groups of frames. The main idea is to use optical flow in combination with a confidence measure to impose robust temporal smoothness constraints. Optimisation is performed using recent advances in the field of graph-cuts combined with practical considerations to reduce run-time and memory consumption. Experimental results with real sequences containing rapid motion demonstrate that the method is able to improve spatio-temporal coherence both in terms of segmentation and reconstruction without introducing any degradation in regions where optical flow fails due to fast motion.