In streaming video completion one aims to fill in missing pixels in streaming video data. This is a problem that naturally arises in the context of surveillance videos. Since these are streaming videos, they must be completed online and in real-time. This makes the streaming video completion problem significantly more difficult than the related video completion problem. State-of-the-art streaming video completion methods based on adaptive matrix completion, do not work well when the number of missing pixels is high (~95%). Therefore, in this report a new streaming video completion method will be introduced based on a tensor-networked Kalman filter. The results in this report will show that this Kalman filter method performs better than the state-of-the-art methods when the percentage of missing pixels is high (~95%).