Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video
This provides a transformative improvement for real-time video surveillance and recognition applications by enabling robust separation on personal laptop-class computing power.
The paper tackles the problem of separating video frames into background and foreground components in real-time by applying dynamic mode decomposition (DMD), achieving results orders of magnitude faster than robust principal component analysis (RPCA) without parameter tuning.
This paper introduces the method of dynamic mode decomposition (DMD) for robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. The method is a novel application of a technique used for characterizing nonlinear dynamical systems in an equation-free manner by decomposing the state of the system into low-rank terms whose Fourier components in time are known. DMD terms with Fourier frequencies near the origin (zero-modes) are interpreted as background (low-rank) portions of the given video frames, and the terms with Fourier frequencies bounded away from the origin are their sparse counterparts. An approximate low-rank/sparse separation is achieved at the computational cost of just one singular value decomposition and one linear equation solve, thus producing results orders of magnitude faster than a leading separation method, namely robust principal component analysis (RPCA). The DMD method that is developed here is demonstrated to work robustly in real-time with personal laptop-class computing power and without any parameter tuning, which is a transformative improvement in performance that is ideal for video surveillance and recognition applications.