1. A computer vision system, comprising: automatically extracting tracks of moving objects on a set of video sequences; choosing a video frame belonging to the track; overlaying onto said video frame, the pixels of selected bounding boxes of a tracked object at key positions along the tracks; and highlighting a bounding box of the overlaid objects; selecting a subset of tracks for training; rendering each training track into a composite image; labeling each track[s] using the rendered composite image[s] in one shot with an operator; training a track classifier by supervised machine learning using the labeled tracks; applying the trained track classifier to the remainder of the tracks; and selecting tracks classified with a low confidence by the classifier for a subsequent labeling; and efficiently labeling video data for training purposes and minimizing operator intervention.
1. A computer vision system, comprising: automatically extracting tracks of moving objects on a set of video sequences; choosing a video frame belonging to the track; overlaying onto said video frame, the pixels of selected bounding boxes of a tracked object at key positions along the tracks; and highlighting a bounding box of the overlaid objects; selecting a subset of tracks for training; rendering each training track into a composite image; labeling each track[s] using the rendered composite image[s] in one shot with an operator; training a track classifier by supervised machine learning using the labeled tracks; applying the trained track classifier to the remainder of the tracks; and selecting tracks classified with a low confidence by the classifier for a subsequent labeling; and efficiently labeling video data for training purposes and minimizing operator intervention. 16. The system of claim 1 , where the objective is a given performance level on a validation set.