Background initialization and foreground segmentation for bootstrapping video sequences

Hsiao, Han-Hui; Leou, Jin-Jang

doi:10.1186/1687-5281-2013-12

Cited by 19 publications

(14 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of the image sequences is used as input to twelve different background reconstruction algorithms [8][9][10][11][12][13][14][15][16][17]. The default settings as suggested by the authors in the respective papers were used for generating the background images.…”

Section: Reconstructed Background Quality (Rebaq) Datasetmentioning

confidence: 99%

“…The default settings as suggested by the authors in the respective papers were used for generating the background images. For the block-based algorithms of [11,14] and [17], the block sizes are set to 8, 16 and 32 to take into account the effect of varying block sizes on the perceived quality of the recovered background. As a result, 18 background images are generated for each of the eight scenes.…”

Section: Reconstructed Background Quality (Rebaq) Datasetmentioning

confidence: 99%

“…The technique of acquiring a clean background image by removing the occlusions using frames from a video or multiple views of a scene is known as background reconstruction or background initialization. Many algorithms have been proposed for initializing the background images from videos, for example [8][9][10][11][12][13][14]; and also from multiple images such as [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Full Reference Objective Quality Assessment for Reconstructed Background Images

Shrotre¹,

Karam²

2018

J. Imaging

View full text Add to dashboard Cite

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.

show abstract

Section: Reconstructed Background Quality (Rebaq) Datasetmentioning

confidence: 99%

Section: Reconstructed Background Quality (Rebaq) Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Full Reference Objective Quality Assessment for Reconstructed Background Images

Shrotre¹,

Karam²

2018

J. Imaging

View full text Add to dashboard Cite

show abstract

“…For examples, superpixels and Markov Random Fields [14], as well as the connected components [15] focus on improving label coherence using advanced regularization techniques. Some other methods rely on the region level [16,17], frame level [18] or hybrid frame-region level [19].…”

Section: Introductionmentioning

confidence: 99%

Robust Background Subtraction via the Local Similarity Statistical Descriptor

et al. 2017

View full text Add to dashboard Cite

Abstract:Background subtraction based on change detection is the first step in many computer vision systems. Many background subtraction methods have been proposed to detect foreground objects through background modeling. However, most of these methods are pixel-based, which only use pixel-by-pixel comparisons, and a few others are spatial-based, which take the neighborhood of each analyzed pixel into consideration. In this paper, inspired by a illumination-invariant feature based on locality-sensitive histograms proposed for object tracking, we first develop a novel texture descriptor named the Local Similarity Statistical Descriptor (LSSD), which calculates the similarity between the current pixel and its neighbors. The LSSD descriptor shows good performance in illumination variation and dynamic background scenes. Then, we model each background pixel representation with a combination of color features and LSSD features. These features are then embedded in a low-cost and highly efficient background modeling framework. The color and texture features have their own merits and demerits; they can compensate each other, resulting in better performance. Both quantitative and qualitative evaluations carried out on the change detection dataset are provided to demonstrate the effectiveness of our method.

show abstract

“…The contribution of the proposed approach is fourfold. First, we propose a threshold-free clustering technique to determine background candidates without requiring parameter tuning to achieve optimal performance [8] [9]. Second, we obtain an initial background estimation (seeds selection) containing more data than state-of-the-art approaches [8] [10] [11] without introducing additional errors.…”

Section: Introductionmentioning

confidence: 99%

Rejection based multipath reconstruction for background estimation in video sequences with stationary objects

Ortego

SanMiguel

Martínez

2016

Computer Vision and Image Understanding

View full text Add to dashboard Cite

This is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, VOL147 (2016) DOI 10.1016/j.cviu.2016.03.012Background estimation in video 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 estimation 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.This work was partially supported by the Spanish Government (HAVideo, TEC2014-53176-R) and by the TEC department (Universidad Autónoma de Madrid)

show abstract

Background initialization and foreground segmentation for bootstrapping video sequences

Cited by 19 publications

References 38 publications

Full Reference Objective Quality Assessment for Reconstructed Background Images

Full Reference Objective Quality Assessment for Reconstructed Background Images

Robust Background Subtraction via the Local Similarity Statistical Descriptor

Rejection based multipath reconstruction for background estimation in video sequences with stationary objects

Contact Info

Product

Resources

About