Method and System for Generating Ground-Truth Annotations of Roadside Objects in Video Data

Abstract

A method and system for generating ground-truth annotations for object detection and classification for roadside objects in video data, wherein the method uses in combination an object detector to detect object instances of roadside objects in each frame of a video, a visual object tracker to detect and track the roadside object across the remaining video frames the roadside object appears in and clusters these detected object instances of the same roadside object into an object track, a trajectory analyzer to filter out object tracks that are unlikely from roadside objects, a classification model to classify each object instance in the object track into a predefined roadside object class, after which the object track as a whole is classified by seeking consensus among the individual object instance classifications in the object track, and classification consistency to determine whether the resulting roadside object class can be assigned automatically to the concerning object track as a ground-truth annotation or whether the ground-truth annotation should be manually verified by an operator. Accordingly, it is possible with the invention to convert model prediction labels in an automated way into ground-truth annotations, so as to create ground-truth annotations with a similar reliability as manual annotation and significantly reduce the amount of manual effort involved in creating reliable ground-truth annotations.

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