Adaptive weighted non-parametric background model for efficient video coding

Chakraborty, Subrata; Paul, Manoranjan; Murshed, Manzur; Ali, Mortuza

doi:10.1016/j.neucom.2016.11.016

Cited by 8 publications

(3 citation statements)

References 31 publications

(41 reference statements)

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“…LRSD-based methods [24][25][26] employ LRSD to decompose the input video into low-rank components representing the background and sparse components representing the moving objects, which are encoded by different methods. Background modeling methods [7,[27][28][29][30] use background modeling technology to build background frames for reference that improve the compression efficiency by improving the prediction accuracy. These surveillance video compression methods only apply to local spatial-temporal redundancy in single-source video; they do not consider the similarity of the background when the same region is captured by multisource satellite videos and cannot cope with apparent differences in the area due to shooting time, posture, height, and other factors.…”

Section: Video Compression Of Surveillance Videosmentioning

confidence: 99%

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Xiao

Zhu

et al. 2018

Remote Sensing

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City surveillance enables many innovative applications of smart cities. However, the real-time utilization of remotely sensed surveillance data via unmanned aerial vehicles (UAVs) or video satellites is hindered by the considerable gap between the high data collection rate and the limited transmission bandwidth. High efficiency compression of the data is in high demand. Long-term background redundancy (LBR) (in contrast to local spatial/temporal redundancies in a single video clip) is a new form of redundancy common in Earth observatory video data (EOVD). LBR is induced by the repetition of static landscapes across multiple video clips and becomes significant as the number of video clips shot of the same area increases. Eliminating LBR improves EOVD coding efficiency considerably. First, this study proposes eliminating LBR by creating a long-term background referencing library (LBRL) containing high-definition geographically registered images of an entire area. Then, it analyzes the factors affecting the variations in the image representations of the background. Next, it proposes a method of generating references for encoding current video and develops the encoding and decoding framework for EOVD compression. Experimental results show that encoding UAV video clips with the proposed method saved an average of more than 54% bits using references generated under the same conditions. Bitrate savings reached 25-35% when applied to satellite video data with arbitrarily collected reference images. Applying the proposed coding method to EOVD will facilitate remote surveillance, which can foster the development of online smart city applications.

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Section: Video Compression Of Surveillance Videosmentioning

confidence: 99%

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Xiao

Zhu

et al. 2018

Remote Sensing

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“…Being non-parametric, WNP outperforms in dynamic background scenarios compared to MoG-based techniques without a priori knowledge of video data distribution. In a further work, Chakraborty et al [64] improved WNP by using a scene adaptive non-parametric (SANP) technique developed to handle video sequences with high dynamic background.…”

Section: Content-based Video Codingmentioning

confidence: 99%

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Bouwmans

García-García²

2019

Preprint

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Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

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“…However, such technological advancement results in an increase in the number, complexity, and size of surveillance videos and this, in turn, entail the need for new algorithms to handle huge video data efficiently and effectively. Background subtraction aims to detect foreground regions that are in motion from background of a video sequence and is a prerequisite of many intelligent video analytics (IVA) applications [1] such as automated video surveillance [2]- [5], optical motion capture [6], [7], computational imaging [8], [9], video inpainting [10]- [12], target tracking [13]- [15], video coding [16], [17], and human-machine interaction [18]. Since the 1990s, researchers have been exploring this field on the subject of different applications.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

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Adaptive weighted non-parametric background model for efficient video coding

Cited by 8 publications

References 31 publications

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

A Comprehensive Survey of Video Datasets for Background Subtraction

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