A Hybrid Approach for Background Subtraction in Video: Combining RPCA, LBP, and Grassmann Average
Keywords:
Histogram equalization, Local Binary Pattern, Robust Principal Component Analysis, Grassmann AverageAbstract
Background subtraction from moving video faces problems such as the complexity of the background, its movement and the change in light intensity arise and fragmented object make it difficult to detect moving objects in video. This paper presents a Novel hybrid model using Robust Principal Component Analysis (RPCA) and LBP (Local Binary Pattern) for background subtraction using Grassmann Average. Grassmann Average (GA) reduces the big outliers and RPCA gives sparse matrix (foreground information) and low rank matrix (back ground information). Feature extraction is done by using Local Binary Pattern (LBP). Finally, proposed RPCA-GA algorithm is executed in CD Net dataset. The results of proposed method are compared with various methods and also yields high Precision and Recall.
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