7,654 research outputs found
An Experimental Evaluation of Foreground Detection Algorithms in Real Scenes
International audience; Foreground detection is an important preliminary step of many video analysis systems. Many algorithms have been proposed in the last years, but there is not yet a consensus on which approach is the most effective, not even limiting the problem to a single category of videos. This paper aims at constituting a first step towards a reliable assessment of the most commonly used approaches. In particular, four notable algorithms that perform foreground detection have been evaluated using quantitative measures to assess their relative merits and demerits. The evaluation has been carried out using a large, publicly available dataset composed by videos representing different realistic applicative scenarios. The obtained performance is presented and discussed, highlighting the conditions under which algorithm can represent the most effective solution
Foreground Detection in Camouflaged Scenes
Foreground detection has been widely studied for decades due to its
importance in many practical applications. Most of the existing methods assume
foreground and background show visually distinct characteristics and thus the
foreground can be detected once a good background model is obtained. However,
there are many situations where this is not the case. Of particular interest in
video surveillance is the camouflage case. For example, an active attacker
camouflages by intentionally wearing clothes that are visually similar to the
background. In such cases, even given a decent background model, it is not
trivial to detect foreground objects. This paper proposes a texture guided
weighted voting (TGWV) method which can efficiently detect foreground objects
in camouflaged scenes. The proposed method employs the stationary wavelet
transform to decompose the image into frequency bands. We show that the small
and hardly noticeable differences between foreground and background in the
image domain can be effectively captured in certain wavelet frequency bands. To
make the final foreground decision, a weighted voting scheme is developed based
on intensity and texture of all the wavelet bands with weights carefully
designed. Experimental results demonstrate that the proposed method achieves
superior performance compared to the current state-of-the-art results.Comment: IEEE International Conference on Image Processing, 201
Foreground detection enhancement using Pearson correlation filtering
Foreground detection algorithms are commonly employed as an initial module in video processing pipelines for automated surveillance. The resulting masks produced by these algorithms are usually postprocessed in order to improve their quality. In this work, a postprocessing filter based on the Pearson correlation among the pixels in a neighborhood of the pixel at hand is proposed. The flow of information among pixels is controlled by the correlation that exists among them. This way, the filtering performance is enhanced with respect to some state of the art proposals, as demonstrated with a selection of benchmark videos.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Full Reference Objective Quality Assessment for Reconstructed Background Images
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.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated
Database:
https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing
(Email for permissions at: ashrotreasuedu
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