Robust Object Tracking via Reverse Low-Rank Sparse Learning and Fractional-Order Variation Regularization

Algorithm 1

Numerical implementation for reverse low-rank sparse learning and fractional-order variation regularization.

	Input: template matrix , dictionary , weight coefficients , , , and .
	Output: .
(1)	Initiate parameters: , , .
(2)	While not converged do
(3)	Fixing other variables to update ; (equation (8))
(4)	Fixing other variables to update ; (equation (11))
(5)	Fixing other variables to update ; (equation (15))
(6)	Updating target template ; (equation (16))
(7)	end