Multi-local-task learning with global regularization for object tracking
作者：
Highlights：
• A multi-local-task learning with global regularization method is proposed.
• Tracking is formulated as a novel multi-local-task learning problem.
• The closed-form solution to the multi-local-task learning is derived.
• The local tasks can assist in addressing the occlusion problem and guide the update.
• The global regularization can constrain the relationship of the local tasks.
摘要
Highlights•A multi-local-task learning with global regularization method is proposed.•Tracking is formulated as a novel multi-local-task learning problem.•The closed-form solution to the multi-local-task learning is derived.•The local tasks can assist in addressing the occlusion problem and guide the update.•The global regularization can constrain the relationship of the local tasks.
论文关键词：Object tracking,Multi-local-task learning,Global regularization
论文评审过程：Received 14 September 2014, Revised 7 June 2015, Accepted 10 June 2015, Available online 24 June 2015, Version of Record 19 August 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.06.005
