Appearances Can Be Deceiving: Learning Visual Tracking from Few Trajectory Annotations
Abstract
Visual tracking is the task of estimating the trajectory of an object in a video given its initial location. This is usually done by combining at each step an appearance and a motion model. In this work, we learn from a small set of training trajectory annotations how the objects in the scene typically move. We learn the relative weight between the appearance and the motion model. We call this weight: visual deceptiveness . At test time, we transfer the deceptiveness and the displacement from the closest trajectory annotation to infer the next location of the object. Further, we condition the transference on an event model. On a set of 161 manually annotated test trajectories, we show in our experiments that learning from just 10 trajectory annotations halves the center location error and improves the success rate by about 10%.
Cite
Text
Manen et al. "Appearances Can Be Deceiving: Learning Visual Tracking from Few Trajectory Annotations." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10602-1_11Markdown
[Manen et al. "Appearances Can Be Deceiving: Learning Visual Tracking from Few Trajectory Annotations." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/manen2014eccv-appearances/) doi:10.1007/978-3-319-10602-1_11BibTeX
@inproceedings{manen2014eccv-appearances,
title = {{Appearances Can Be Deceiving: Learning Visual Tracking from Few Trajectory Annotations}},
author = {Manen, Santiago and Kwon, Junseok and Guillaumin, Matthieu and Van Gool, Luc},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {157-172},
doi = {10.1007/978-3-319-10602-1_11},
url = {https://mlanthology.org/eccv/2014/manen2014eccv-appearances/}
}