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The ability to predict future states is crucial to informed decision-makingwhile interacting with dynamic environments. With cameras providing a prevalentand information-rich sensing modality, the problem of predicting future statesfrom image sequences has garnered a lot of attention. Current state-of-the-artmethods typically train large parametric models for their predictions. Thoughoften able to predict with accuracy these models often fail to provideinterpretable confidence metrics around their predictions. Additionally thesemethods are reliant on the availability of large training datasets to convergeto useful solutions. In this paper, we focus on the problem of predictingfuture images of an image sequence with interpretable confidence bounds fromvery little training data. To approach this problem, we use non-parametricmodels to take a probabilistic approach to image prediction. We generateprobability distributions over sequentially predicted images, and propagateuncertainty through time to generate a confidence metric for our predictions.Gaussian Processes are used for their data efficiency and ability to readilyincorporate new training data online. Our methods predictions are evaluated ona smooth fluid simulation environment. We showcase the capabilities of ourapproach on real world data by predicting pedestrian flows and weather patternsfrom satellite imagery.

Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes