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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Appearances Can Be Deceiving: Learning Visual Tracking from Few Trajectory Annotations
verfasst von : Santiago Manen, Junseok Kwon, Matthieu Guillaumin, Luc Van Gool
Erschienen in: Computer Vision – ECCV 2014
Verlag: Springer International Publishing
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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%.