  Training data is a critical requirement for machine learning tasks, and
labeled training data can be expensive to acquire, often requiring manual or
semi-automated data collection pipelines. For tracking applications, the data
collection involves drawing bounding boxes around the classes of interest on
each frame, and associate detections of the same "instance" over frames. In a
semi-automated data collection pipeline, this can be achieved by running a
baseline detection and tracking algorithm, and relying on manual correction to
add/remove/change bounding boxes on each frame, as well as resolving errors in
the associations over frames (track switches). In this paper, we propose a data
correction pipeline to generate ground-truth data more efficiently in this
semi-automated scenario. Our method simplifies the trajectories from the
tracking systems and let the annotator verify and correct the objects in the
sampled keyframes. Once the objects in the keyframes are corrected, the
bounding boxes in the other frames are obtained by interpolation. Our method
achieves substantial reduction in the number of frames requiring manual
correction. In the MOT dataset, it reduces the number of frames by 30x while
maintaining a HOTA score of 89.61% . Moreover, it reduces the number of frames
by a factor of 10x while achieving a HOTA score of 79.24% in the SoccerNet
dataset, and 85.79% in the DanceTrack dataset. The project code and data are
publicly released at
https://github.com/foreverYoungGitHub/trajectory-simplify-benchmark.
