Block-Based Quantized Histogram (BBQH) for efficient background modeling and foreground extraction in video
01 Feb 2017-pp 224-229
TL;DR: This paper proposes an efficient way of background modeling and elimination for extracting foreground information from the video, applying a new block-based statistical feature extraction technique coined as Block Based Quantized Histogram (BBQH) for background modeling.
Abstract: This paper proposes an efficient way of background modeling and elimination for extracting foreground information from the video, applying a new block-based statistical feature extraction technique coined as Block Based Quantized Histogram (BBQH) for background modeling. The inclusion of contrast normalization and anisotropic smoothing in the preprocessing step, makes the feature extraction procedure more robust towards several unorthodox situations like illumination change, dynamic background, bootstrapping, noisy video and camouflaged conditions. The experimental results on the benchmark video frames clearly demonstrate that BBQH has successfully extracted the foreground information despite the various irregularities. BBQH also gives the best F-measure values for most of the benchmark videos in comparison with the other state of the art methods, and hence its novelty is well justified.
Citations
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TL;DR: In this paper, a survey of background subtraction methods used in real applications is presented, in order to identify the real challenges met in practice, the current used background models and to provide future directions.
Abstract: 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 (i.e CCD cameras, omnidirectional cameras, …), 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.
141 citations
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TL;DR: This work identifies the background models that are effectively used in 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.
Abstract: 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.
132 citations
TL;DR: 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.
Abstract: 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.
41 citations
29 Jan 2018
TL;DR: A new method was proposed for improving the performance in detecting passing persons with high accuracy and high stability in the background subtraction algorithm by combining with an inter-frame subtraction method and a mode image method.
Abstract: A new method was proposed for improving the performance in detecting passing persons with high accuracy and high stability in the background subtraction algorithm. The method, Improved Background Quick Updater (IBQU), was structured by combining with an inter-frame subtraction method and a mode image method. The excellent performance of the proposed method was achieved by changing the background according to state of the passing person detection. Additional feedback loop with a switch dramatically improved the performance in detecting a stopping person. The proposed method was successfully applied to detect a person walking, stopping halfway and walking again.
1 citations
29 Jul 2018
TL;DR: A new background subtraction method was proposed for detecting baggage left in a crowded public space with high accuracy and high stability and was successfully applied to detecting a left bag.
Abstract: A new background subtraction method was proposed for detecting baggage left in a crowded public space with high accuracy and high stability. Those properties came from the combination of an inter-frame subtraction method with a mode image method. The background was rapidly updated by the former and was also slowly changed by the latter. The combination of them enabled us to extract moving object with high accuracy. A newly added switching process eliminated pixels extracted as the moving object from the calculation for the mode values as the background. Thus, the background was kept while the object was extracting. The switching process was reset, when the difference between the observation and the background fell below a threshold. The proposed method was successfully applied to detecting a left bag.
1 citations
References
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TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >
12,560 citations
"Block-Based Quantized Histogram (BB..." refers methods in this paper
...Unlike noise smoothing filters, generally perform smoothing operations in the whole image area (irrespective of low and high frequency region), the anisotropic filter proposed by Perona and Malik [13] provides the facility of smoothing of the low frequency regions and emphasizes the edge regions using Eq....
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...Thus, background modeling has a greater impact in automatic video analysis system, since a proper background modeling and elimination will provide effective foreground information for different intelligent video applications like activity recognition, object tracking, motion capture, video surveillance [10], [12], [13], [17] etc....
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23 Jun 1999
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Abstract: A common method for real-time segmentation of moving regions in image sequences involves "background subtraction", or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian, distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.
7,660 citations
"Block-Based Quantized Histogram (BB..." refers background in this paper
...333, which is the best among the list, and the second average rank is held by GMM(SG)[11], which is 3....
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...Several techniques have been proposed in [1], [2], [3], [4], [6], [7], [9], [10], [11], [12], [14], which model the background based statistical measure of the static pixels (the intensity values of the pixels remaining unchanged between the frames) in the previously observed frames....
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TL;DR: A real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task and demonstrates the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.
Abstract: We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system deals in particularly with detecting when interactions between people occur and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth. Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach. We propose and compare two different state-based learning architectures, namely, HMMs and CHMMs for modeling behaviors and interactions. Finally, a synthetic "Alife-style" training system is used to develop flexible prior models for recognizing human interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.
1,831 citations
01 Jan 2002
TL;DR: This paper presents a method which improves this adaptive background mixture model by reinvestigating the update equations at different phases, which allows the system learn faster and more accurately as well as adapts effectively to changing environment.
Abstract: Real-time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance, human-machine interface, and very low-bandwidth telecommunications A typical method is background subtraction Many background models have been introduced to deal with different problems One of the successful solutions to these problems is to use a multi-colour background model per pixel proposed by Grimson et al [1, 2,3] However, the method suffers from slow learning at the beginning, especially in busy environments In addition, it can not distinguish between moving shadows and moving objects This paper presents a method which improves this adaptive background mixture model By reinvestigating the update equations, we utilise different equations at different phases This allows our system learn faster and more accurately as well as adapts effectively to changing environment A shadow detection scheme is also introduced in this paper It is based on a computational colour space that makes use of our background model A comparison has been made between the two algorithms The results show the speed of learning and the accuracy of the model using our update algorithm over the Grimson et al’s tracker When incorporate with the shadow detection, our method results in far better segmentation than The Thirteenth Conference on Uncertainty in Artificial Intelligence that of Grimson et al
1,638 citations
"Block-Based Quantized Histogram (BB..." refers background in this paper
...The similarity in the intensity changes among pixels is considered in [8], where the pixels are classified into several clusters based on the similarity of their intensity changes....
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TL;DR: This work presents recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel and presents a simple non-parametric adaptive density estimation method.
Abstract: We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel. We also present a simple non-parametric adaptive density estimation method. The two methods are compared with each other and with some previously proposed algorithms.
1,483 citations