Background Update Research Articles

GOCO06s – a satellite-only global gravity field model

Abstract. GOCO06s is the latest satellite-only global gravity field model computed by the GOCO (Gravity Observation Combination) project. It is based on over a billion observations acquired over 15 years from 19 satellites with different complementary observation principles. This combination of different measurement techniques is key in providing consistently high accuracy and best possible spatial resolution of the Earth's gravity field. The motivation for the new release was the availability of reprocessed observation data for the Gravity Recovery and Climate Experiment (GRACE) and Gravity field and steady-state Ocean Circulation Explorer (GOCE), updated background models, and substantial improvements in the processing chains of the individual contributions. Due to the long observation period, the model consists not only of a static gravity field, but comprises additionally modeled temporal variations. These are represented by time-variable spherical harmonic coefficients, using a deterministic model for a regularized trend and annual oscillation. The main focus within the GOCO combination process is on the proper handling of the stochastic behavior of the input data. Appropriate noise modeling for the observations used results in realistic accuracy information for the derived gravity field solution. This accuracy information, represented by the full variance–covariance matrix, is extremely useful for further combination with, for example, terrestrial gravity data and is published together with the solution. The primary model data consisting of potential coefficients representing Earth's static gravity field, together with secular and annual variations, are available on the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/, last access: 11 June 2020). This data set is identified with the following DOI: https://doi.org/10.5880/ICGEM.2019.002 (Kvas et al., 2019b). Supplementary material consisting of the full variance–covariance matrix of the static potential coefficients and estimated co-seismic mass changes is available at https://ifg.tugraz.at/GOCO (last access: 11 June 2020).

Protection of visual privacy in videos acquired with RGB cameras for active and assisted living applications

Active and assisted living technologies are much needed, but some aspects of them cause user rejection due to concerns on privacy. This is even more concerning to users when visual information is used, processed, and transmitted. To respond to these concerns, and maximise user acceptance, visual privacy protection measures have to be put in place. In the past, human detection and object segmentation in video were constrained by technological limitations, and could only run with specific hardware and sensors. This paper introduces a proposal for an RGB-only based visual privacy preservation filter, which capitalises on ‘deep learning’-based segmentation and pose detectors. A background update scheme is presented, which limits leakage of sensitive information when detection fails. Dilation of the mask can further prevent information leakage, but a trade-off is necessary to correctly update background information. This is achieved via a specific study which is also presented. A comparative study is performed to determine the best configuration for privacy preservation. Results show that union of dilated masks from different deep networks achieves the best overall result.

Fast background subtraction with adaptive block learning using expectation value suitable for real-time moving object detection

This paper presents a method of moving object detection through a fast background subtraction technique suitable for real-time performance in wide range of platforms. An intermittent background update using adaptive blocks individually calculates the learning rate through expected difference values. Then, coupled with a fast background subtraction process, the design achieves fast throughput with well-rounded performance. To compensate for the lagging effects of intermittent background update, an adaptation bias is devised to improve precision and recall metrics. Experiments show a versatile performance in varying scenes with overall results better than conventional techniques. The proposed method achieved a fast execution speed of up to 56 fps in PC using Full HD video. It also achieved 655 fps and 83 fps in PC and ARM core-embedded platform, respectively, using the minimum input resolution of 320 × 240. Overall, it is suitable for real-time performance applications.

An Improved Near-Field Computer Vision for Jet Trajectory Falling Position Prediction of Intelligent Fire Robot.

An improved Near-Field Computer Vision (NFCV) system for intelligent fire robot was proposed that was based on our previous works in this paper, whose aims are to realize falling position prediction of jet trajectory in fire extinguishing. Firstly, previous studies respecting the NFCV system were briefly reviewed and several issues during application testing were analyzed and summarized. The improved work mainly focuses on the segmentation and discrimination of jet trajectory adapted to complex lighting environment and interference scenes. It mainly includes parameters adjustment on the variance threshold and background update rate of the mixed Gaussian background method, jet trajectory discrimination based on length and area proportion parameters, parameterization, and feature extraction of jet trajectory based on superimposed radial centroid method. When compared with previous works, the proposed method reduces the average error of prediction results from 1.36 m to 0.1 m, and the error variance from 1.58 m to 0.13 m. The experimental results suggest that every part plays an important role in improving the functionality and reliability of the NFCV system, especially the background subtraction and radial centroid methods. In general, the improved NFCV system for jet trajectory falling position prediction has great potential for intelligent fire extinguishing by fire-fighting robots.

A novel background updation algorithm using fuzzy c-means clustering for pedestrian detection

The task of pedestrian detection in video surveillance applications will face challenges like dynamic background changes, false human detection (shadow), and illumination variations. In literature, many approaches have been proposed to resolve these challenges. But their performance is not up to the mark. Thus this paper proposes efficient pedestrian detection including shadow removal and automatic dynamic background update. For this firstly, a background frame is initialized where no moving object is present. Then a background subtraction algorithm is applied to each of the key frames from the live video to detect the foreground objects (using fuzzy C means clustering followed by mean absolute difference). Later on this segmented foreground a contour is estimated and passed through the HOG classifier for pedestrian detection. The performance of the proposed approach has been compared using various datasets & state-of-the-art approaches and found to the best with an average precision of 98 %, unlike the others.

Comprehensive Chemical Characterization of the Pistacia vera Fruits through Original NMR Quantification Methods

(1) Background: Pistacia vera is a dry fruit spread all over the world, commonly known as pistachio, and is very appreciated as a snack, as a precious ingredient for confectionery industries and also as a blessed touch for gourmet dishes; (2) Methods: The simultaneous NMR profiles concerning the hydrophilic and hydrophobic phases, enabled by a specific throughput over grinded pistachio seeds, led to the comprehensive chemical characterization of these nuts coming from different territories; (3) Results: The customized extraction method is described in detail as well as the employed nuclear magnetic resonance (NMR) experiments. The chemical quantification takes advantage from the innovative multi-assignment recovered analysis (MARA-NMR) technique able to provide experimentally robust panels for both hydrophobic and hydrophilic chemicals. Beyond the assessed repeatability and the interesting statistical discrimination, this paper adds information about the quantitative and qualitative composition of pistachio as inferred by NMR data. This updated background can pave the way toward the development of wider and wider chemical panels improving the knowledge about the pistachio’s composition. This approach can be easily extended to other matrices.

Flue gas layer feature segmentation based on multi-channel pixel adaptive

It is very difficult to accurately separate the smoke contour in fire video. Because the scene of a fire is complex and changeable, and there are often many interference factors, such as continuously changing light and fast switching scenes, it is difficult to accurately guarantee the separation of smoke contours. In this paper, an aggregate channel feature algorithm that combines color, saturation and texture is designed, and a fast pixel block feature matching method is used to build the background model. In order to overcome the error caused by scene switching, a dynamic threshold control method based on the background switching speed is proposed, which eliminates the interference caused by the dynamic background update, and effectively extracts the foreground smoke pixels and smoke contour map. The experimental results show that the algorithm can accurately extract the smoke layer contour map, and compared with the traditional foreground extraction algorithms, the algorithm is faster and more accurate.

First molecular epidemiological study of hepatitis B and D in individuals infected with human T-lymphotropic virus 1/2 from Argentina.

Human T-lymphotropic virus 1/2 (HTLV-1/2), hepatitis B virus (HBV), and hepatitis D virus (HDV) share transmission routes. Argentina shows low prevalence of HTLV-1/2, HBV, and HDV infections; however, this situation may vary according to the geographic region and group studied. The aim of this study was to estimate the prevalence of HBV and HDV infections and detect both viral genotypes in HTLV-1/2 individuals from Argentina. A total of 202 HTLV-1/2 confirmed samples (blood donors [BD] and individuals with risk factors for HTLV-1/2 [RF]) were tested for HBsAg and total anti-HBc by enzyme-linked immunosorbent assay. All reactive samples for some HBV markers were analyzed for HBV DNA characterization and HDV serological and molecular analysis. Total prevalence was 1.5% for HBsAg and 6.4% for anti-HBc. Prevalence was 23.1% for anti-HDV in all HBV-reactive samples. No significant difference was observed for HBV and HDV prevalence within HTLV subtypes. The population study showed that prevalence of anti-HBc was higher in the RF than in the BD population, with no significant differences between them. The HBsAg marker and anti-HDV were only found in RF, showing significant differences when compared to BD. Regarding molecular detection, one sample amplified for HBV DNA and none for HDV RNA. HBV sequence was classified as subgenotype F1b. New and updated background on serological markers of HBV and HDV infection in patients with HTLV-1/2 was provided.

2019 IUSTI‐Europe guideline for the management of anogenital warts

This guideline is an update of the 2011 European Guideline for the Management of Anogenital Warts. It is intended to support best practice in the care of patients with anogenital warts by including evidence-based recommendations on diagnosis, treatment, follow-up and advice to patients. It is intended for use by healthcare professionals in sexual healthcare or dermato-venereology clinics in Europe but may be adapted for use in other settings where the management of anogenital warts is undertaken. As a European guideline, recommendations should be adapted according to national circumstances and healthcare systems. Despite the availability of vaccine to prevent HPV types 6 and 11, the cause of >95% anogenital warts, they remain an important and frequent health problem. The previous systematic review of randomized controlled trials for anogenital warts was updated. The changes in the present guideline include the following: Updated background information on the prevalence, natural history and transmission of human papillomavirus (HPV) infection and anogenital warts. Key recommendations for diagnosis and treatment have been graded according to the strength of the recommendation and the quality of supporting evidence. 5-fluorouracil, local interferon and photodynamic therapy have been evaluated and included as potential second-line treatment options. Evidence of the impact of HPV vaccination on the incidence of anogenital warts has been updated.

Retracted: Design of Target Detection and Tracking System for Sports Video

Video motion target tracking has been widely used in the field of sports. Content-based video search has a broad application prospect and attracts more and more researchers’ attention. In view of the shortcomings of moving object detection in motion video at present, a human motion recognition method based on correlation vector machine is proposed. Combined with the improved Gaussian mixture model of moving object detection and tracking method in motion video, moving object detection and tracking are carried out. The design of the system is realized. Firstly, it collects sports videos, considers all kinds of sports objects in sports videos, uses improved background update difference method to detect sports objects, then uses correlation vector machine to recognize human motion, and finally uses AdaBoost classifier to use it in multiple sports videos and sports objects. Carry out follow-up experiments. Results in this way, we can track the sports target in sports video accurately and quickly, and the real-time performance of sports target tracking is better than other tracking methods, which provides a new research tool for tracking sports target in sports video.

Online Rail Surface Inspection Utilizing Spatial Consistency and Continuity

Rail surface inspection using visual inspection system is an important part of railway maintenance. However, accurate and efficient identification of possible defects remains challenging. This paper proposes a background-oriented defect inspector (BODI) to improve defect detection by considering specified characteristics of the track during inspection. Reformulating the inspection task in this manner offers a new way to model rail surface images. More specifically, BODI features a random sampling stage to obtain a compact background representation without any prior information. A sufficient number of random selections generates adequate and diverse background statistics, and defect-determination and a fusion of procedures then determine whether current pixel belongs to the background. Finally, a background update mechanism and parallelism ensure real-time applicability. The proposed BODI is evaluated on a working railway line. The experimental results demonstrate that it outperforms state-of-the-art methods.

Multiplicatively Regularized Iterative Updated Background Inversion Method for Inverse Scattering Problems

A multiplicatively regularized iterative updated background method (MR-IUBM) based on difference integral equations is proposed to solve the electromagnetic inverse scattering problems (ISPs) involving both inhomogeneous and homogeneous background media. This aims to improve the ability for solving highly nonlinear ISPs with source-typed inversion solver by consistently updating the inhomogeneous background media from the updated unknown scatterers via the use of the Green's function with homogeneous medium. In virtue of MR into the cost function, the stability and the capability of tackling highly nonlinear ISPs can be further greatly improved. Synthetic and experimental results are presented to illustrate the efficiency of this method.

Moving vehicle video detection combining ViBe and inter-frame difference

It is difficult with traditional methods to realise real-time and robust detection of moving vehicles under complex traffic scenes. In this paper, a moving vehicle video detection method that combines ViBe and inter-frame difference is proposed. The proposed method improves the background update efficiency of the traditional ViBe method by adding a multi-threshold comparison step to the inter-frame difference method. The improved background update strategy can judge whether the detected pixel point belongs to the foreground or background, and dynamically adjusts the background update rate according to the inter-frame difference results. Experimental results showed the proposed method can effectively remove of ghosting phenomenon that occurs in traditional ViBe method and realise accurate and complete detection of the moving vehicle in video.

A Research on Dynamic Target Tracking with Camera Based on Kalman Filter

With the popularization of the camera, all kinds of video network communication technologies have developed towards civil use and become increasingly popular. For household application, a common PC is used to obtain the frame data from the home network, while the constant frame is set as the background. The update function is used to update background in real time, and thereby forming an inter-frame relation matrix. Based on the matrix, Kalman filter can match and track targets. This method can effectively complete the calculation on the PC, perform precise detection and deal with the complexity of the household application. Finally, with the test results, this paper further proves that the method is feasible and can be used in actual situations.

ВИКОРИСТАННЯ ІНТЕРАКТИВНИХ МЕТОДІВ НАВЧАННЯВ ПРОЦЕСІ ПІДГОТОВКИ МАЙБУТНІХ ПЕДАГОГІВ

In the article essence of interactive teaching methods is explained in the article and the advantages of their application in the educational process of higher education institutions are argued. The importance of the focus of the modern educational process not only on professional but also on personal growth of students is outlined. It is about the need to create conditions for the formation of personality traits such as mobility, ability to integrate into a dynamic society, critical thinking, the ability to generate new ideas, the ability to make non-standard decisions and be responsible for them, communication skills, teamwork skills, skills educational situations, etc. The essence of interactive learning is defined, which consists in mutual learning, group form of organization of educational process with realization of active group teaching methods for solving didactic problems. The peculiarities of using interactive teaching methods in the process of preparation of future teachers are revealed. The essence of the most common interactive teaching methods is considered. Working in pairs is an effective method that involves sharing ideas with a partner and further voicing your thoughts to the audience. It is effective to use the Microphone method to update background knowledge, which enables each student to say something quickly, alternately, by answering questions or expressing their opinion or position. Collective discussion, such as Brainstorming, is at the forefront of the educational process of higher education. The Carousel method is most effective for simultaneously involving all participants in the educational process in active work with different partners to discuss discussion issues. The Learning-to-Learn method enables students to pass on their knowledge to their classmates. The method of projects that determines the acquisition and application of practical skills by students. An important tool for the cognitive activity of future professionals is a discussion that involves broad public discussion of some controversial issue. The role of interactive teaching methods in the process of formation of personal and professional qualities of future teachers is revealed.

Model Update Strategies about Object Tracking: A State of the Art Review

Object tracking has always been an interesting and essential research topic in the domain of computer vision, of which the model update mechanism is an essential work, therefore the robustness of it has become a crucial factor influencing the quality of tracking of a sequence. This review analyses on recent tracking model update strategies, where target model update occasion is first discussed, then we give a detailed discussion on update strategies of the target model based on the mainstream tracking frameworks, and the background update frameworks are discussed afterwards. The experimental performances of the trackers in recent researches acting on specific sequences are listed in this review, where the superiority and some failure cases on each of them are discussed, and conclusions based on those performances are then drawn. It is a crucial point that design of a proper background model as well as its update strategy ought to be put into consideration. A cascade update of the template corresponding to each deep network layer based on the contributions of them to the target recognition can also help with more accurate target location, where target saliency information can be utilized as a tool for state estimation.

Probabilistic static foreground elimination for background subtraction

ABSTRACTBackground subtraction is generally used to detect moving objects, because it has low complexity, and it is easy to implement. However, the detection error increases when the background is changing. Therefore, adaptive background subtraction is applied to overcome this problem, and it continuously requires updating the background with a fixed learning rate. The learning rate should be tuned for a consistently evolving background. This paper proposes the Probabilistic Static Foreground Elimination for Background Subtraction (PSFE) algorithm. It consisted of two parameters: the number of frames for static foreground elimination, and the probability of changes in background pixels. These two parameters can tune the learning rate and update background for better detection. The average results of the detection error rate from Wallflower datasets were tested with PSFE and well-known method. They demonstrated that PSFE provides moving object detection with minimum detection error (5.95%), especially in Camouflage, Moved object, and Light switch dataset.

Community sound levels associated with Allianz Field

Allianz Field, a 20 000 seat outdoor stadium in St. Paul, MN is the new home of the Minnesota United Loons MLS team. At the start of the project, community ambient levels were initially measured near three residential areas for the environmental review process. Although exposed to noise from a nearby interstate highway and arterial roadways, sound levels were within Minnesota noise standards. Following completion of the stadium, updated background readings were taken to reflect changes in three years since the initial ambient levels were measured. Sound levels were then monitored during the first night game at the venue. Readings showed that crowd level barely exceeded ambient levels and were below the Minnesota standards. Stadium sound system levels were not audible. An evaluation of the potential noise impacts from predicted concert noise was then prepared. While a database of maximum mix levels at 100 feet from various groups was available, these values had to be converted to L10 levels governed by the state standards. Examination of the time history data for selected events provided needed conversion from mix peak to L10. Concert levels projected at the three original monitoring locations show some initial standard violations. Calculating backward in the prediction process showed that concerts with a mix level of 90 dBA would be in compliance with daytime standards while a mix limit of 80 dBA would suffice for late evening performances.

A new spatio-temporal background–foreground bimodal for motion segmentation and detection in urban traffic scenes

Automatic vehicle detection in urban traffic surveillance is an important and urgent issue, since it provides necessary information for further processing. Conventional techniques utilize either motion segmentation or appearance-based detection, which involves either poor adaptation or high computation. The complexity of urban traffic scenarios lies in slow motion temporarily stopped or parked vehicles, dynamic background, and sudden illumination variations. In this paper, a new motion segmentation technique is proposed based on spatio-temporal background–foreground bimodal. The temporal background information is modeled using a weighted sigma–delta estimation, cumulative frame differencing is used to model the foreground pixels, and the spatial correlation between neighboring pixels is utilized to combine both background and foreground models. The median of consecutive frame difference adapts sudden illumination variation, update background model, and reinitialize foreground model. Comparative experimental results for typical urban traffic sequences show that the proposed technique achieves robust and accurate segmentation, which improves adaptation, reduce false detection, and satisfy real-time requirements.

Background subtraction via time continuity and texture consistency constraints.

Background subtraction has attracted enormous interest in the field of moving object detection. However, when there are complex scenarios such as illumination changes, dynamic background, and noise, the moving object area obtained by background subtraction often has holes, noise, and shadows. This paper proposes a novel background update model based on matrix factorization, which uses the temporal continuity of video content to solve the problems of holes, noise, and shadows. Moreover, in some cases, the texture consistency of the object is also a factor worth considering. The neighborhood weighed local binary pattern (NWLBP) is introduced to optimize the background update model, which is very effective for suppressing background or foreground shadow. The effectiveness of our method is confirmed by extensive experiments on public data sets and real shot video. Compared with the existing state-of-the-art moving object detection methods, the proposed methods can accurately establish the background model and locate the moving object region robustly.