  Annotating object ground truth in videos is vital for several downstream
tasks in robot perception and machine learning, such as for evaluating the
performance of an object tracker or training an image-based object detector.
The accuracy of the annotated instances of the moving objects on every image
frame in a video is crucially important. Achieving that through manual
annotations is not only very time consuming and labor intensive, but is also
prone to high error rate. State-of-the-art annotation methods depend on
manually initializing the object bounding boxes only in the first frame and
then use classical tracking methods, e.g., adaboost, or kernelized correlation
filters, to keep track of those bounding boxes. These can quickly drift,
thereby requiring tedious manual supervision. In this paper, we propose a new
annotation method which leverages a combination of a learning-based detector
(SSD) and a learning-based tracker (RE$^3$). Through this, we significantly
reduce annotation drifts, and, consequently, the required manual supervision.
We validate our approach through annotation experiments using our proposed
annotation method and existing baselines on a set of drone video frames. Source
code and detailed information on how to run the annotation program can be found
at https://github.com/robot-perception-group/smarter-labelme
