Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle
Temporal slowness principle is exploited for learning tracking representation. Learned invariant representation is decomposed into amplitude and phase features. Higher-level features are learned by stacking autoencoders convolutionally. A novel observational model to counter drift and collect relevant samples online. Tracking experiments show our method is superior to state-of-the-art trackers.
self-taught learning invariant representation visual tracking temporal slowness deep learning