Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance
Résumé
Foreground detection is the first step in video surveillance system to detect moving objects. Recent research on
subspace estimation by sparse representation and rank minimization represents a nice framework to separate moving
objects from the background. Robust Principal Component Analysis (RPCA) solved via Principal Component Pursuit
decomposes a data matrix A in two components such that A = L + S , where L is a low-rank matrix and S is a sparse
noise matrix. The background sequence is then modeled by a low-rank subspace that can gradually change over
time, while the moving foreground objects constitute the correlated sparse outliers. To date, many efforts have been
made to develop Principal Component Pursuit (PCP) methods with reduced computational cost that perform visually
well in foreground detection. However, no current algorithm seems to emerge and to be able to simultaneously
address all the key challenges that accompany real-world videos. This is due, in part, to the absence of a rigorous
quantitative evaluation with synthetic and realistic large-scale dataset with accurate ground truth providing a balanced
coverage of the range of challenges present in the real world. In this context, this work aims to initiate a rigorous
and comprehensive review of RPCA-PCP based methods for testing and ranking existing algorithms for foreground
detection. For this, we first review the recent developments in the field of RPCA solved via Principal Component
Pursuit. Furthermore, we investigate how these methods are solved and if incremental algorithms and real-time
implementations can be achieved for foreground detection. Finally, experimental results on the Background Models
Challenge (BMC) dataset which contains different synthetic and real datasets show the comparative performance of
these recent methods.