Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle
作者：
Highlights：
• 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.
摘要
Highlights•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.
论文关键词：Visual tracking,Temporal slowness,Deep learning,Self-taught learning,Invariant representation
论文评审过程：Received 20 June 2014, Revised 9 January 2015, Accepted 17 February 2015, Available online 26 February 2015, Version of Record 17 June 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.02.012
