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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
Convolutional networks reach top quality in pixel-level object tracking but
require a large amount of training data (1k ~ 10k) to deliver such results. We
propose a new training strategy which achieves state-of-the-art results across
three evaluation datasets while using 20x ~ 100x less annotated data than
competing methods. Instead of using large training sets hoping to generalize
across domains, we generate in-domain training data using the provided
annotation on the first frame of each video to synthesize ("lucid dream")
plausible future video frames. In-domain per-video training data allows us to
train high quality appearance- and motion-based models, as well as tune the
post-processing stage. This approach allows to reach competitive results even
when training from only a single annotated frame, without ImageNet
pre-training. Our results indicate that using a larger training set is not
automatically better, and that for the tracking task a smaller training set
that is closer to the target domain is more effective. This changes the mindset
regarding how many training samples and general "objectness" knowledge are
required for the object tracking task.
