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We propose a framework for semi-automated annotation of video frames wherethe video is of an object that at any point in time can be labeled as being inone of a finite number of discrete states. A Hidden Markov Model (HMM) is usedto model (1) the behavior of the underlying object and (2) the noisyobservation of its state through an image processing algorithm. The key insightof this approach is that the annotation of frame-by-frame video can be reducedfrom a problem of labeling every single image to a problem of detecting atransition between states of the underlying objected being recording on video.The performance of the framework is evaluated on a driver gaze classificationdataset composed of 16,000,000 images that were fully annotated over 6,000hours of direct manual annotation labor. On this dataset, we achieve a 13xreduction in manual annotation for an average accuracy of 99.1% and a 84xreduction for an average accuracy of 91.2%.

Semi-Automated Annotation of Discrete States in Large Video Datasets