Recent studies on optical flow typically focus on the estimation of the single flow field in-between a pair of images but pay little attention to the multiple consecutive flow fields in a longer video sequence. In this paper, we propose an efficient video optical flow estimation method by exploiting the temporal coherence and context dynamics under a Kalman filtering system.In this system, pixel’s motion flow is first formulated as a second order time-variant state vector, and then optimally estimated according to the measurement and system noise levels within the system by maximum a posterior criteria. Specifically, we evaluate the measurement noise according to the flow’s temporal derivative, spatial gradient, and warping error. And we determine the system noise based on the similarity of contextual information,which is represented by the compact features learned by pre-trained convolutional neural networks. The context-aware Kalman filtering helps improve the robustness of our method against abrupt change of light and occlusion/dis-occlusion in complicated scenes. Experimental results and analyses on theMPI Sintel, Monkaa and Driving video datasets demonstrate that the proposed method performs favorably against the state-of-the art approaches.
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