  Moving object detection is critical for automated video analysis in many
vision-related tasks, such as surveillance tracking, video compression coding,
etc. Robust Principal Component Analysis (RPCA), as one of the most popular
moving object modelling methods, aims to separate the temporally varying (i.e.,
moving) foreground objects from the static background in video, assuming the
background frames to be low-rank while the foreground to be spatially sparse.
Classic RPCA imposes sparsity of the foreground component using l1-norm, and
minimizes the modeling error via 2-norm. We show that such assumptions can be
too restrictive in practice, which limits the effectiveness of the classic
RPCA, especially when processing videos with dynamic background, camera jitter,
camouflaged moving object, etc. In this paper, we propose a novel RPCA-based
model, called Hyper RPCA, to detect moving objects on the fly. Different from
classic RPCA, the proposed Hyper RPCA jointly applies the maximum correntropy
criterion (MCC) for the modeling error, and Laplacian scale mixture (LSM) model
for foreground objects. Extensive experiments have been conducted, and the
results demonstrate that the proposed Hyper RPCA has competitive performance
for foreground detection to the state-of-the-art algorithms on several
well-known benchmark datasets.
