Object Tracking via Jointing Mixed Norm Constrained and Incremental Non-negative Matrix Factorization
Chen Yu · Jisuanji gongcheng · 2015
Aiming at the problems of the tracking drift tracking caused by illumination changes,partial occlusions,pose changes and background clutter and other factors,an object tracking algorithm of jointing mixed norm constraints and Incremental Non-negative Matrix Factorization(INMF)is presented.The local structure information of the target is gained by the non negative matrix factorization,which is effective to deal with the partial occlusion,and achieves the purpose of reducing the dimension.The interference of the external environment is further suppressed by the mixed norm of sparse description,and the optimization problem is solved iteratively by using the accelerated approximate gradient algorithm.In order to better meet the demand of realtime and accurate tracking,the occlusion detection and online update policy are presented to read the location of tracking object.The algorithm puts into particle filter tracking framework for robust target tracking algorithm is proposed.Experimental results show that the proposed algorithm performs favorably against IVT,multi-instance learning,Frag and L1 APG tracking algorithms.