Abstract: A system and method for generating hard training data from easy training data. Training data including visual data with synthetic semantic implants (“VSSI”) having at least one cue is received. An annotator identifies at least one cue in the VSSI and annotates the VSSI to indicate the cue to create a modified training data set. A data scrambler removes at least one cue from the VSSI to create the tagged training data, which can then be used to train a classifier to identify transitions between segments when the cues are not present.

Abstract: Methods and systems for annotating an object in a video frame. An annotation of an object in a video frame is obtained and a plurality of computerized object trackers are run on the annotated object to automatically label the annotated object in a plurality of subsequent video frames. A computerized user interface aggregating results of the automatic labeling by the plurality of computerized object trackers is presented and, based on the computerized user interface, an identification of a best one of the plurality of computerized object trackers is obtained. The selected computerized object tracker is used to annotate the object in the plurality of subsequent video frames.

Abstract: Methods and systems for annotating an object in a video frame. An annotation of an object in a video frame is obtained and a plurality of computerized object trackers are run on the annotated object to automatically label the annotated object in a plurality of subsequent video frames. A computerized user interface aggregating results of the automatic labeling by the plurality of computerized object trackers is presented and, based on the computerized user interface, an identification of a best one of the plurality of computerized object trackers is obtained. The selected computerized object tracker is used to annotate the object in the plurality of subsequent video frames.