Title :
Practical ReProCS for separating sparse and low-dimensional signal sequences from their sum — Part 2
Author :
Han Guo ; Vaswani, Namrata ; Chenlu Qiu
Author_Institution :
Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA
Abstract :
In this work, we experimentally evaluate and verify model assumptions for our recently proposed algorithm (practical ReProCS) for recovering a time sequence of sparse vectors St and a time sequence of dense vectors Lt from their sum, Mt := St + Lt, when Lt lies in a slowly changing low-dimensional subspace. A key application where this problem occurs is in video layering where the goal is to separate a video sequence into a slowly changing background sequence and a sparse foreground sequence that consists of one or more moving regions/objects. Practical-ReProCS is the practical analog of its theoretical counterpart that was studied in our recent work.
Keywords :
image sequences; sparse matrices; video signal processing; Practical-ReProCS; low-dimensional signal sequences; sparse foreground sequence; sparse signal sequences; sparse vectors; time sequence; video layering; Big data; Information processing; Lakes; Principal component analysis; Robustness; Vectors; Video sequences; robust PCA; robust matrix completion; sparse recovery;
Conference_Titel :
Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on
Conference_Location :
Atlanta, GA
DOI :
10.1109/GlobalSIP.2014.7032141