Self-Motion-Assisted Tensor Completion Method for Background Initialization in Complex Video Sequences

doi:10.1109/TIP.2019.2946098

2020-01-01

PRE

Ibrahim Kajo

Nidal Kamel *

Yassine Ruichek

DOI：10.1109/TIP.2019.2946098

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摘要

En 中文

The background Initialization (BI) problem has attracted the attention of researchers in different image/video processing fields. Recently, a tensor-based technique called spatiotemporal slice-based singular value decomposition (SS-SVD) has been proposed for background initialization. SS-SVD applies the SVD on the tensor slices and estimates the background from low-rank information. Despite its efficiency in background initialization, the performance of SS-SVD requires further improvement in the case of complex sequences with challenges such as stationary foreground objects (SFOs), illumination changes, low frame-rate, and clutter. In this paper, a self-motion-assisted tensor completion method is proposed to overcome the limitations of SS-SVD in complex video sequences and enhance the visual appearance of the initialized background. With the proposed method, the motion information, extracted from the sparse portion of the tensor slices, is incorporated with the low-rank information of SS-SVD to eliminate existing artifacts in the initiated background. Efficient blending schemes between the low-rank (background) and sparse (foreground) information of the tensor slices is developed for scenarios such as SFO removal, lighting variation processing, low frame-rate processing, crowdedness estimation, and best frame selection. The performance of the proposed method on video sequences with complex scenarios is compared with the top-ranked state-of-the-art techniques in the field of background initialization. The results not only validate the improved performance over the majority of the tested challenges but also demonstrate the capability of the proposed method to initialize the background in less computational time.

Keyword:

Lighting

Spatiotemporal phenomena

Video sequences

Feature extraction

Clutter

Data mining

Background initialization

tensor completion

spatiotemporal slice

stationary foreground object

illumination changes