Practical temporal consistency for image-based graphics applications
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Improving the robustness of variational optical flow through tensor voting
Computer Vision and Image Understanding
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
On performance analysis of optical flow algorithms
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Adaptive integration of feature matches into variational optical flow methods
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Reconstructing detailed dynamic face geometry from monocular video
ACM Transactions on Graphics (TOG)
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Despite the fact that temporal coherence is undeniably one of the key aspects when processing video data, this concept has hardly been exploited in recent optical flow methods. In this paper, we will present a novel parametrization for multi-frame optical flow computation that naturally enables us to embed the assumption of a temporally coherent spatial flow structure, as well as the assumption that the optical flow is smooth along motion trajectories. While the first assumption is realized by expanding spatial regularization over multiple frames, the second assumption is imposed by two novel first- and second-order trajectorial smoothness terms. With respect to the latter, we investigate an adaptive decision scheme that makes a local (per pixel) or global (per sequence) selection of the most appropriate model possible. Experiments show the clear superiority of our approach when compared to existing strategies for imposing temporal coherence. Moreover, we demonstrate the state-of-the-art performance of our method by achieving Top 3 results at the widely used Middlebury benchmark.