Improved Background Subtraction Research Articles

Detecting Steam Leakage in Nuclear Power Systems Based on the Improved Background Subtraction Method

As a key system in nuclear power plants, nuclear power systems contain high-temperature, high-pressure water media. A steam leak, if it occurs, can at minimum cause system functional loss and at worst lead to casualties. Therefore, it is urgent to carry out steam leakage detection work for high-temperature, high-pressure loop systems. Currently, steam leaks are primarily detected through visual monitoring and pressure gauges. However, if there is a minor leak under high system pressure, the slight decrease in pressure may not be enough to alert the operators, leading to a delay in detecting the steam leak. Thus, this detection method has certain drawbacks. In view of these issues, this paper introduces computer vision technology to monitor the high-temperature, high-pressure loop system and proposes the use of an improved background subtraction method to detect steam leaks in the loop system. The results show the following advantages of this method: (1) It can effectively identify steam leaks at an early stage; (2) it overcomes the difficulty of determining the threshold value for the binarization of grayscale images in traditional background subtraction methods; (3) it eliminates the noise impact brought by the binarization of grayscale images in existing improved background subtraction methods. The introduction of this method provides a new approach for detecting steam leaks in high-temperature, high-pressure loop systems and can be effectively applied in engineering fields. It also offers reference value for the detection of high-temperature, high-pressure media leaks in other fields.

Improved Background Subtraction with Histogram Equalization and Adaptive Thresholding

Improved Background Subtraction with Histogram Equalization and Adaptive Thresholding

New Approach of Generating Ground-Truth for Local Surveillance Dataset Tested with Benchmark Background Subtraction Models

Background subtraction is the dominant approach in the domain of moving object detection. Lots of research has been done to design or improve background subtraction models. However, there are a few well-known and state-of-the-art models that can be applied as a benchmark. Generally, these models are applied to different dataset benchmarks. Most of the time, choosing an appropriate dataset is challenging due to the lack of dataset availability and the tedious process of creating ground-truth frames for the sake of quantitative evaluation. Therefore, in this article, we collected local video scenes of a street and river taken by a stationary camera, focusing on dynamic background challenges. We presented a new technique for creating ground-truth frames using modeling, composing, tracking, and rendering each frame. Eventually, we applied three promising algorithms used in this domain: GMM, KNN, and ViBe, to our local dataset. Results obtained by quantitative evaluations revealed the effectiveness of our new technique for generating the ground-truth scenes to be benchmarked with the original scenes using a number of statistical metrics.

Improved background subtraction and a fresh look at jet sub-structure in JEWEL

Interactions of hard partons in the Quark Gluon Plasma (QGP) created with relativistic heavy ion collisions lead to characteristic modifications of the internal structure of reconstructed jets. A large part of the observed jet sub-structure modifications stem from the QGP’s response to energy and momentum deposited by hard partons. Good control over medium response in theoretical calculations is thus instrumental to a quantitative understanding of medium modified (quenched) jets in heavy ion collisions. We present an improved way of handling the medium response in the jet quenching model Jewel and present results for a variety of jet sub-structure observables. The new recoil handling is more versatile and robust than the old scheme, giving a better control over many observables and, in particular, greatly improves the description of the jet mass.

Comparative Analysis of Background Subtraction Models Applied on a Local Dataset Using a New Approach for Ground-truth Generation

Abstract— Background subtraction is the dominant approach in the domain of moving object detection. Lots of research have been done to design or improve background subtraction models. However, there is a few well known and state of the art models which applied as a benchmark. Generally, these models are applied on different dataset benchmarks. Most of the time Choosing appropriate dataset is challenging due to the lack of datasets availability and the tedious process of creating the ground-truth frames for the sake of quantitative evaluation.
 Therefore, in this article we collected local video scenes for street and river taken by stationary camera focusing on dynamic background challenge. We presented a new technique for creating ground-truth frames using modelling, composing, tracking, and rendering each frame. Eventually we applied nine promising benchmark algorithms used in this domain on our local dataset. Results obtained by quantitative evaluations exposed the effectiveness of our new technique for generating the ground-truth scenes to be benchmarked with the original scenes using number of statistical metrics. Furthermore, results shows the outperformance of SuBSENSE model against other tested models.

Detection of Change in Body Motion With Background Construction and Silhouette Orientation

Background subtraction techniques have been widely implemented and improvised to obtain a stable background model. The novelty of the proposed work is to generate a stable background model under dynamic changes in the environmental conditions where a) an improved background subtraction algorithm is proposed based on GMM with EM algorithm for computing granulometry and run faster for the generation of a stable background model; b) Detecting the foreground by curvelet based denoising process with improvised semisoft thresholding techniques with morphological operations; c) background maintenance is done by an adaptive algorithm in which the intensity values are mapped to remove the connected components with less than P pixels. The proposed scheme works for the Spatio-temporal motion of the object in both spatial and temporal modes. The experimental outcome for the proposed model results in the accurate shape analysis of the object in motion thereby dipping the complexity

Improved Background Subtraction Technique for Detecting Moving Objects

Introduction: Moving object detection from videos is among the most difficult task in different areas of computer vision applications. Among the traditional object detection methods, researchers conclude that Background Subtraction method carried out better in aspects of execution time and output quality. Mehtod: Visual background extractor is a renowned algorithm in Background Subtraction method for detecting moving object in various applications. In the recent years, lots of work has been carried out to improve the existing Visual Background extractor algorithm. Result: After investigating many state of art techniques and finding out the research gaps, this paper presents an improved background subtraction technique based on morphological operation and 2D median filter for detecting moving object which reduces the noise in the output video and also enhances its accuracy at a very limited additional cost. Experimental results in several benchmark datasets confirmed the superiority of the proposed method over the state-of-the-art object detection methods. Conclusion: In this article, a method has been proposed for moving object detection where the quality of the output object is enhanced and good accuracy is achieved. This method provide with accurate experimental results, which helps in efficient object detection. The proposed technique also deals with Visual Background extractor Algorithm along with the Image Enhancement Procedure like Morphological and 2-D Filtering at a limited additional cost Discussion: This article worked on certain specific field, like noise reduction and image enhancement of output images of the existing ViBe Algorithm. The technique proposed in this article will be beneficial for various computer vision applications like video surveillance, road condition monitoring, airport safety, human activity analysis, monitoring marine border for security purpose etc.

An Improved Background Subtraction Method for Adaptive Rate Compressive Sensing

In Compressive Video Sensing application, the using of Adaptive Rate Compressive Sensing (ARCS) method can predict and adjust the sampling rate for each video frame, reduce the total sampling rate and improve the quality of reconstructed image. In order to use the inter-frame correlation of the video signal efficiently and reduce the sampling rate of the whole signal, an Improved Background Subtraction (IBS) method is proposed in this paper. By alternately using the frame without any foreground objects and the previous frame as the background of the signal, the sparsity of the foreground signal is improved and the total sampling rate is reduced. Experimental results show that, compare with the traditional background subtraction method, the IBS method can significantly reduce the total video sampling rate without apparent degradation of reconstructed image quality under the same ARCS method.

Moving object detection of assembly components based on improved background subtraction algorithm

In the automatic assembly of mechanical components, it is difficult to guarantee the quality of assembly image acquisition caused by the change of assembly environment. Firstly, the color space is compared and analysed, and histogram equalization is used to preprocess the image to reduce the impact of image brightness on subsequent processing. Then, based on the texture features of the image, an image reconstruction idea from coarse to fine is created, and an image representation method from macro to micro is established to suppress the pseudo texture and noise texture in image processing. On this basis, an adaptive threshold algorithm based on the target distance is proposed to improve the detection accuracy of the assembly target. Experimental results show that the proposed algorithm can significantly improve the detection accuracy of moving objects in assembly video frames.

Asynchronous Semantic Background Subtraction.

The method of Semantic Background Subtraction (SBS), which combines semantic segmentation and background subtraction, has recently emerged for the task of segmenting moving objects in video sequences. While SBS has been shown to improve background subtraction, a major difficulty is that it combines two streams generated at different frame rates. This results in SBS operating at the slowest frame rate of the two streams, usually being the one of the semantic segmentation algorithm. We present a method, referred to as “Asynchronous Semantic Background Subtraction” (ASBS), able to combine a semantic segmentation algorithm with any background subtraction algorithm asynchronously. It achieves performances close to that of SBS while operating at the fastest possible frame rate, being the one of the background subtraction algorithm. Our method consists in analyzing the temporal evolution of pixel features to possibly replicate the decisions previously enforced by semantics when no semantic information is computed. We showcase ASBS with several background subtraction algorithms and also add a feedback mechanism that feeds the background model of the background subtraction algorithm to upgrade its updating strategy and, consequently, enhance the decision. Experiments show that we systematically improve the performance, even when the semantic stream has a much slower frame rate than the frame rate of the background subtraction algorithm. In addition, we establish that, with the help of ASBS, a real-time background subtraction algorithm, such as ViBe, stays real time and competes with some of the best non-real-time unsupervised background subtraction algorithms such as SuBSENSE.

Crowd gathering and commotion detection based on the stillness and motion model

The abnormal event detection becomes an important topic recently. This paper presents a method to detect the crowd gathering, as well as the commotion event after the crowd gathering. The proposed stillness model and the motion model are based on the improved background subtraction and the optical flow feature. We construct the long-term stillness level by the break bucket model and clustering the instantaneous stillness level. Then the crowd gathering event is decided by the threshold with the long-term stillness level. Furthermore, the motion model is applied for detecting the commotion event after the crowd gathering. In the experiment, we used the dataset of PET2009. The proposed method is verified by the experiment with 97% accuracy.

Hierarchical Improvement of Foreground Segmentation Masks in Background Subtraction

A plethora of algorithms have been defined for foreground segmentation, a fundamental stage for many computer vision applications. In this paper, we propose a post-processing framework to improve the foreground segmentation performance of background subtraction algorithms. We define a hierarchical framework for extending segmented foreground pixels to undetected foreground object areas and for removing erroneously segmented foreground. First, we create a motion-aware hierarchical image segmentation of each frame that prevents merging foreground and background image regions. Then, we estimate the quality of the foreground mask through the fitness of the binary regions in the mask and the hierarchy of segmented regions. Finally, the improved foreground mask is obtained as an optimal labeling by jointly exploiting foreground quality and spatial color relations in a pixel-wise fully connected conditional random field. Experiments are conducted over four large and heterogeneous data sets with varied challenges (CDNET2014, LASIESTA, SABS, and BMC) demonstrating the capability of the proposed framework to improve background subtraction results.

EFF-ViBE: An Efficient and Improved Background Subtraction Approach based on ViBE

EFF-ViBE: An Efficient and Improved Background Subtraction Approach based on ViBE

Improved background subtraction method for detecting moving objects based on GMM

The background subtraction (BS) method is one of the most commonly used components in the detection of moving objects in computer vision systems, in which moving objects in image sequences are detected by comparing the background model with the current frame. Traditional moving object detection algorithms have some problems, such as changes in dynamic background, background variation due to changes of illumination, and being more sensitive to noise and shadows. In this study, an improved BS method based on the Gaussian mixture model (GMM) is proposed for the challenge in object detection. Not only is the influence of changes of illumination, and noise and shadows reduced, but the dynamic changes of natural scenes can also be tackled by using this method. The contribution lies in the following aspects: (i) a Gaussian background modeling method with less running time is proposed in the background modeling stage. The background is reconstructed based on GMM of the mean images of image blocks, aiming to simplify the calculations so as to improve the speed of the corresponding operations. (ii) In the foreground detection stage, a wavelet‐based, denoising method with the semisoft threshold function is applied to denoise the object images of the foreground. (iii) In the background maintenance stage, an adaptive background maintenance algorithm is proposed to dynamically update the background. Experimental results show that the computational complexity is reduced, while the adaptability and performance are improved by using this method. It outperforms the traditional methods in terms of both efficiency and robustness. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

一种改进的基于卡尔曼滤波的背景差分算法

一种改进的基于卡尔曼滤波的背景差分算法

Secured Authentication through Integration of Gait and Footprint for Human Identification

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

An improved background subtraction method for HRI based on image parameters

Purpose – Background subtraction is a particularly popular foreground detection method, whose background model can be updated by using input images. However, foreground object cannot be detected accurately if the background model is broken. In order to improve the performance of foreground detection in human-robot interaction (HRI), the purpose of this paper is to propose a new background subtraction method based on image parameters, which helps to improve the robustness of the existing background subtraction method. Design/methodology/approach – The proposed method evaluates the image and foreground results according to the image parameters representing the change features of the image. It ignores the image that is similar to the first image and the previous image in image sequence, filters the image that may break the background model and detects the abnormal background model. The method also helps to rebuild the background model when the model is broken. Findings – Experimental results of typical interaction scenes validate that the proposed method helps to reduce the broken probability of background model and improve the robustness of background subtraction. Research limitations/implications – Different threshold values of image parameters may affect the results in different environments. Future researches should focus on the automatic selection of parameters’ threshold values according to the interaction scene. Practical implications – A useful method for foreground detection in HRI. Originality/value – This paper proposes a method which employs image parameters to improve the robustness of the background subtraction for foreground detection in HRI.

An Improved Moving Object Detection Algorithm Based on Colour Separation

An improved background subtraction algorithm based on color separation method is improved to solve the problem of high rate of missing detection of moving targets in video surveillance. First, the algorithm implements the background subtraction of the current frame and the background frame for pixel blocks in independent RGB channels. Then the three foreground targets after denoising extracted respectively from three channels are combined by or operation to get a complete moving target. And then this algorithm removes the shadow in color model. After that the background frame from each channel is updated individually by recursive algorithm. The algorithm adopts a nonlinear way to change the learning factor for background updating. The experiments show that this improved algorithm can extract effectively foreground targets which are similar to the background’s grey level, with better accuracy and robustness.

Background subtraction using spatiotemporal condition information

To improve background subtraction performance in dynamic scenes, a novel background subtraction method using spatiotemporal condition information (SCI) is proposed. To obtain SCI, a spatiotemporal neighborhood was constructed based on the center surround visual saliency model. To reduce the errors in the background subtraction result, a neighborhood weighted spatiotemporal condition information (NWSCI) was utilized for pixel classification by considering the similarity of neighborhood pixels. A joint cascade and hierarchical framework was introduced to reduce computational cost by rejecting the unchanged regions in videos before background subtraction with NWSCI. Experimental results show that the proposed method can effectively detect moving object in dynamic scenes in real time, and performs better compared to the existing methods with lower resource cost.

A Moving Object Detection Algorithm Based On Multiple Judgments

In the field of moving object detection, the traditional background subtraction method is used broadly, which seems more sensitive to light and shows poor anti-interference performance. Background modeling is the key step of background subtraction method. The Local Binary Pattern (LBP) algorithm is considered to put texture information into the background model, combining color and texture information and an improved background subtraction method proposed. In addition, a new method is proposed combining the inter-frame difference method with improved background subtraction method in this paper. It can overcome traditional methods only using the pixel gray value changes for moving targets detection. The method makes use of dual-threshold to detect moving targets and makes multiple judgments. It not only uses the change of pixel gray value to detect moving targets, but also takes advantage of the number of changed pixels to detect moving targets which we are interested in. The experiments show that the algorithm proposed is adopted to detect the moving target accurately and can resist interferences brought about by the slow slight movements in the scene with better robustness. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3338