Rejection-based Multipath Reconstruction for
Background Initialization in Video Sequences with Stationary Objects
[Description][Abstract][Dataset][References]
Description
This page provides the
the generated ground-truth data of the proposed video sequences for the task of Background Initialization. Additionally,
the developed software to compute a background image from a video sequence is provided. This material has been used in our submitted paper to Computer Vision and Image Understanding.
Moreover, the SBMI2015 dataset [1] has also been used for evaluation.
Contact Information
Diego Ortego - show
email
Abstract
Background initialization consists in extracting a foreground-free image from a set of training frames.
Moving and stationary objects may affect the background visibility, thus invalidating the assumption
of many related literature where background is the temporal dominant data. In this paper, we present
a temporal-spatial block-level approach for background initialization in video to cope with moving
and stationary objects. First, a Temporal Analysis module obtains a compact representation of the
training data by motion filtering and dimensionality reduction. Then, a threshold-free hierarchical
clustering determines a set of candidates to represent the background for each spatial location (block).
Second, a Spatial Analysis module iteratively reconstructs the background using these candidates. For
each spatial location, multiple reconstruction hypotheses (paths) are explored to obtain its neighboring
locations by enforcing inter-block similarities and intra-block homogeneity constraints in terms of color
discontinuity, color dissimilarity and variability. The experimental results show that the proposed approach
outperforms the related state-of-the-art over challenging video sequences in presence of moving
and stationary objects.
Dataset
Ground-truth is available here
Results in the proposed dataset and in SBMI2015 dataset are available (proposed and top-ranked algorithms from the literature) here
Software is available here
Currently, software download is disabled as the paper is
under review.
References
[1] L. Maddalena, A. Petrosino, "Towards Benchmarking Scene Background Initialization"
in V. Murino et al. (eds), New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops, Lecture Notes
in Computer Science, vol. 9281, pp. 469–476, 2015.