Efficient local monitoring approach for the task of background subtraction

Farou, Brahim; Kouahla, Mohamed Nadjib; Séridi, Hamid; Akdag, Herman

doi:10.1016/j.engappai.2017.05.013

Cited by 10 publications

(4 citation statements)

References 61 publications

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“…The video frames were also manually annotated at every 5 seconds throughout the sequence. One published work that uses this dataset to improve the performance of the background subtraction algorithm in complicated video scenes is [131].…”

Section: B Rgb-d Rigid Multi-body Datasetmentioning

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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Section: B Rgb-d Rigid Multi-body Datasetmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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“…Moreover, it is also interesting to consider improvements of the current used models (MOG [341], codebook [187], ViBe [28], PBAS [142]). Instead of the original MOG, codebook and ViBe algorithms employed as in most of the reviewed works in this paper, several improvements of MOG [242][189] [110][404] [307][232] as well as codebook [421][321] [199][415], ViBe [152][134] [403][427] and PBAS [167] algorithms are potential usable methods for these real applications. For example, Goyal and Singhai [120] evaluated six improvements of MOG on the CDnet 2012 dataset showing that Shah et al's MOG [318] and Chen et Ellis'MOG [72] both published in 2014 achieve significantly better detection while being usable in real applications than previously published MOG algorithms, that are MOG in 1999, Adaptive GMM P1C2-MOG-92 in 2003, Zivkovic-Heijden GMM [430] in 2004, and Effective GMM [204] in 2005.…”

Section: Solved and Unsolved Challengesmentioning

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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“…In [28], a speed computation algorithm of object detection is proposed which first removes noisy pixels and then applies some adaptive thresholds to catch moving objects as a foreground extraction. However, based on [29]- [33], even they propose new techniques but the problem of separating noisy background pixels in an outdoor environment remains present. The proposed threshold adaptation and XOR accumulation (TAXA) algorithm works based on how much information is available that surrounds each pixel; this information can decide whether the pixel belongs to the foreground or background; the decision is made according to the novelty use of XOR-theory for crucial adaptive thresholds of statistic techniques as shown in section 3 with detail.…”

Section: Introductionmentioning

confidence: 99%

Threshold adaptation and XOR accumulation algorithm for objects detection

Kurmasha

Ali

2022

IJECE

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<span>Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean and gaussian for the detection of the pixels of the objects. The third is a novel step in making decisions by using XOR-accumulation to extract pixels of the objects from the thresholds accurately. Each stage was presented with practical representations and theoretical explanations. On high resolution video which has difficult scenes and lighting conditions, the proposed algorithm was used and tested. As a result, with a precision average of 0.90% memory uses of 6.56% and the use of CPU 20% as well as time performance, the result excellent overall superior to all the major used foreground object extraction algorithms. As a conclusion, in comparison to other popular OpenCV methods the proposed TAXA algorithm has excellent detection ability.</span>

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Efficient local monitoring approach for the task of background subtraction

Cited by 10 publications

References 61 publications

A Comprehensive Survey of Video Datasets for Background Subtraction

A Comprehensive Survey of Video Datasets for Background Subtraction

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

Threshold adaptation and XOR accumulation algorithm for objects detection

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