Visual Object Tracking Algorithm Based on ML and L2-Norm
Jiang Ming-xi · Dianzi xuebao · 2013
The tracking of target is a challenging issue in computer vision.In this paper,we propose a visual object tracking algorithm based on ML estimation and L2-norm.Firstly,the model of sparsity constrained ML is established.Abnormal pixels in the samples will be assigned with low weights to reduce their affects on the tracking algorithm.Then,L2-norm minimization is used to solve the sparse coding.Finally,the object tracking results is obtained using Bayesian MAP estimation.Compared with other popular methods,our proposed method reduces the computational complexity and has stronger robustness to abnormal changes(e.g.occlusion,rotation,scale change,illumination,etc.)