Background Update Research Articles

Moving Target Detection and Tracking

This study proposes a background update algorithm based on the correlation coefficient, overcome the shortcomings of fuzzy image of the average background updating. Then, it uses subtraction background method to detect moving targets and record the moving target region and use projection histogram to resize the center of the target and calculate the confidence coefficient of the current frame and the subsequent frame to research the center of the target in the subsequent frames. Then, the moving target could be tracked in the subsequent frames. The experimental results show that the algorithm can accurately detect and automatically track moving targets.

Motion Target Detection of Birds Based on Adaptive Background Update Mechanism

In view of the natural environment variability and uncertainty makes observations of some birds behavior complex and difficult, this paper proposes a Surendra adaptive background updating algorithm based on global threshold, which can update background automatically according to the environmental changes, and then extract the birds target region of interested by background subtraction and morphological filtering. The experimental results show that the birds moving target detection of video-captured has a good extraction effect.

A Method for Real-Time Medaka (<i>Oryzias latipes</i>) Detection Base on Machine Vision Technology

The Japanese medaka (Oryzias latipes) behavior monitoring system based on computer vision is very effective in the field of monitoring the safety of drinking water. But it is very difficult to detect the fishs body accurately and rapidly. In order to get the whole data of medaka in the real-time video sequence, a new approach to detect medaka is proposed in this paper which is based on 2D auto-threshold (OTSU), frame difference and background subtraction. With this method, the pixels in the image are divided into four parts: foreground, ghosting, foreground aperture and the others, then update background base on the data of the third part. At last, the whole fishs body can be got by some postprocessor. These methods are proved to be efficient to get the clean foreground and detect the fish in the real-time fish monitoring video.

The Method of Background Extraction and Background Updating Based on Traffic Video

In order to improve the degree and real-time of the vehicle image detection, a background extraction method based on the probability mean value method and the background update based on the weighted coefficient method through divided area are proposed through the acquisition of real-time traffic information and processing of video images for intelligent transportation systems. Finally a prototype of background extraction and background update is got, and it achieves the detection of moving vehicles. The experimental results show that this method is simple, small amount of calculation and it has a good robustness; it can extract a good background image quickly and detect a complete shadow of vehicles. So this method can meet the requirements of real-time detection of multiple moving targets.

Inland Ship Recognition Algorithm Based on Neighboring Static Region Substitution for Background Updating

Considering the particular scene that the moving ships in Inland River are surrounded by the nearly static water, this paper presents a method that updates the background by adopting the neighboring static region to replace the dynamic areas. By making the difference between the current frame and the background image to get the foreground area, and using the region growing algorithm to shape a complete ship region, we can get the coordinate, the center point and the direction of its movement. To obtain its mean gray value to replace the foreground dynamic region, we choose a static adjacent background at front-end of the ship. Then we adopt a variable weight method to update background in order to prepare for the next image proceeding background difference calculation. As the loop goes on, we can make fast and accurate ship image identification from the surveillance streaming media.

A Novel Method of Motion People Detection and Tracking in Moving Background

The Surendra background update and novel fast model matching were mixed which can reduce the matching region. A Yuntai tracking system was present for people tracking, the fuzzy control tracking based on polar coordinates was also present, which makes the tracking of people object always in the video range and the Yuntai neednt move frequency. Results indicate that the algorithm is superior to the previously published variants of the model matching and the Yuntai system track people in real time.

Foreground detection using loopy belief propagation

This paper develops a simple and effective method for detection of sows and piglets in grayscale video recordings of farrowing pens. This approach consists of three stages: background updating, calculation of pseudo-wavelet coefficients and foreground object segmentation. In the first stage, the texture integration is used to update the background modelling (i.e. the reference image). In the second stage, we apply an “a trous” wavelet transform on the current reference image and then perform subtraction between the current original image and the approximation of the current reference image. In the third stage, the pairwise relationships between a pixel and its neighbours on a factor graph are modelled based on the pseudo-wavelet coefficients, and the image probabilities are approximated by using loopy belief propagation. Experiments have shown promising results in extracting foreground objects from complex farrowing pen scenes, such as sudden light changes and dynamic background as well as motionless foreground objects.

Exercise Standards for Testing and Training

The purpose of this report is to provide revised standards and guidelines for the exercise testing and training of individuals who are free from clinical manifestations of cardiovascular disease and those with known cardiovascular disease. These guidelines are intended for physicians, nurses, exercise physiologists, specialists, technologists, and other healthcare professionals involved in exercise testing and training of these populations. This report is in accord with the “Statement on Exercise” published by the American Heart Association (AHA).1 These guidelines are a revision of the 1995 standards of the AHA that addressed the issues of exercise testing and training.2 An update of background, scientific rationale, and selected references is provided, and current issues of practical importance in the clinical use of these standards are considered. These guidelines are in accord with the American College of Cardiology (ACC)/AHA Guidelines for Exercise Testing.3 ### The Cardiovascular Response to Exercise Exercise, a common physiological stress, can elicit cardiovascular abnormalities that are not present at rest, and it can be used to determine the adequacy of cardiac function. Because exercise is only one of many stresses to which humans can be exposed, it is more appropriate to call an exercise test exactly that and not a “stress test.” This is particularly relevant considering the increased use of nonexercise stress tests. ### Types of Exercise Three types of muscular contraction or exercise can be applied as a stress to the cardiovascular system: isometric (static), isotonic (dynamic or locomotory), and resistance (a combination of isometric and isotonic).4,5 Isotonic exercise, which is defined as a muscular contraction resulting in movement, primarily provides a volume load to the left ventricle, and the response is proportional to the size of the working muscle mass and the intensity of exercise. Isometric exercise is defined as a muscular contraction without movement (eg, handgrip) and imposes greater pressure than volume …

Human Motion Analysis Based On Silhouette and Centroid Displacement

Human motion analysis in an intelligence surveillance system is a hot research topic in computer vision field.In this paper we proposed a motion recognition method based on silhouette information and centroid displacement for static environment.We used background subtraction method added background update and did binarization processing on foreground image by using adaptive threshold segmentation technique,then extracted moving target from the videos.Last we used silhouette information and centroid displacement as human motion features for behaviour analysis.Experiments show that the method has high accuracy rate at daily behaviour recognition.

Video Object Detection Algorithm Based on Gaussian Mixture Models

The moving object detection of the video image is the basis of sequence image analysis, and it is the research hot issue of today’s foreign and domestic scholars. For detecting the moving object from the scene image in time, a detection algorithm of video moving object based on Gaussian mixture models is proposed in the paper. The pixel values are seen as the combination of the foreground Gaussian distribution and the background Gaussian distribution, and the background estimation and the adaptive background update will be put up. The statistical number of the foreground pixel of the current frame determines whether the light has a larger change, and it combines with the frame-difference method to detect moving object. The experimental results show that the algorithm can quickly and accurately establish the background model and accurately segment the foreground object.

Moving Objects Detection Based on Gaussian Models with HSV

An important step in video tracking and identification is moving target detection. The new method combined Gaussian background models with HSV remove space shadow, better adapted to the situation of background changed and updated, avoid segmentation errors caused by shadow. First, the Gaussian model was constructed, and a new way was adopted to update background. Then, a coarse foreground image could be obtained by background subtraction. Next, moving shadows was eliminated by a shadow detection algorithm using hue, saturation and brightness information. Finally, we performed post processing, including morphology filtering, connected component analysis and seeded region growing. The experiments prove using the detection results and mean shift algorithm can get a better tracking.

Improvement of the surface pressure operator in GRAPES and its application in precipitation forecasting in South China

In this study we investigated the problems involved in assimilating surface pressure in the current global and regional assimilation and prediction system, GRAPES. A new scheme of assimilating surface pressure was proposed, including a new interpolation scheme and a refreshed background covariance. The new scheme takes account of the differences between station elevation and model topography, and it especially deals with stations located at elevations below that of the first model level. Contrast experiments were conducted using both the original and the new assimilation schemes. The influence of the new interpolation scheme and the updated background covariance were investigated. Our results show that the new interpolation scheme utilized more observations and improved the quality of the mass analysis. The background covariance was refreshed using statistics resulting from the technique proposed by Parrish and Derber in 1992. Experiments show that the updated vertical covariance may have a positive influence on the analysis at higher levels of the atmosphere when assimilating surface pressure. This influence may be more significant if the quality of the background field at high levels is poor. A series of assimilation experiments were performed to test the validity of the new scheme. The corresponding simulation experiments were conducted using the analysis of both schemes as initial conditions. The results indicated that the new scheme leads to better forecasting of sea level pressure and precipitation in South China, especially the forecast of moderate and heavy rain.

Scalable-Width Temporal Edge Detection for Recursive Background Recovery in adaptive background modeling

Background modeling is a preliminary processing task that generates background or reference frame for moving object detection. Apart from tracking background scene, a good quality background model will prevent false detection. Fuzzy Running Average (FRA) is an efficient background modeling scheme which employs a Fuzzy Inference System (FIS). Its high selectivity in background update prevents foreground object from appearing in the reference frame. Later, Extended Fuzzy Running Average (EFRA) was developed to allow FRA to recover the occlusion if a background object starts moving. However, the recovery rate of EFRA is limited due to the use of fixed width for detecting the occlusion's edge. In this paper, a newly developed method based on Scalable-Width Temporal Edge Detection (SWTED) is proposed to enhance the EFRA performance in locating and recovering the occlusion with higher rate. The results obtained show that the improved EFRA significantly outperforms FRA in background tracking. The algorithm is also well suited for real-time implementation.

Robust Object Tracking Via Class Aware Partial Least Squares-gabor Wavelet Subspace

Effective tracking is still a big challenge due to lack of robust descriptors which captures discriminative features in non-controlled environment. We propose a novel descriptor based on Gabor wavelet and Partial Least Squares (PLS) discriminant analysis. Multi scale and multi orientation Gabor wavelets can extract selective local frequencies effectively in spatial and frequency domain. Due to the large dimension of feature vectors, dimensionality reduction is done using class aware PLS analysis. Unlike unsupervised Principal Component Analysis (PCA), PLS based subspace model learns target effectively by explicitly knowing the class labels of target and background region feature vectors. Tracking is done using particle filter and similarity between target and candidates is measured using low dimensional subspace model. To combat the target changes during tracking, novel static and dynamic target as well as background update strategy is used. Experimental results of various dataset demonstrate that the proposed tracker improves robustness and accuracy against representative trackers.

Reduction of Pixel Occurrences in Background Updating

Abstract The paper presents the modification of background generation algorithm based on analysis of the frequency of occurrences of pixels. The proposed solution allows the generation of the background and its updating, the introduced parameter allows to adjust the algorithm according to the time rate of changes in the image. The results show that the modified method can be applied in many tasks related to the detection and analysis of moving objects.

Moving Object Detection and Tracking Algorithm Based on Background Subtraction

Moving target detection and tracking algorithm as the core issue of computer vision and human-computer interaction is the first step of intelligent video surveillance system. Through comparing temporal difference method and background subtraction, a moving object detection and tracking algorithm based on background subtraction under static background is proposed, in order to quickly and accurately detect and identify the moving object in the intelligent monitoring system. In this algorithm, firstly, we use background acquisition method to receive the background image, then use the current frame image and the received background image to perform background subtraction in order to extract foreground object information and receive the difference image; secondly, we use threshold segmentation and morphology image processing to process the difference image in order to eliminate noises and receive the clear binary moving object image; finally, we use the centroid tracking method to track and mark the moving object. Experimental results show that the algorithm can effectively and quickly detect and track moving object from video sequence under static background. This algorithm is easily realized and has good real-time and robust, which is automated and self triggered for background updating. The algorithm can be used in driver assistance systems, motion capture, virtual reality and other fields.

A More Robust Mean Shift Tracker on Joint Color-CLTP Histogram

A more robust mean shift tracker using the joint of color and Completed Local Ternary Pattern (CLTP) histogram is proposed. CLTP is a generalization of Local Binary Pattern (LBP) which can be applied to obtain texture features that are more discriminant and less sensitive to noise. The joint of color and CLTP histogram based target representation can exploit the target structural information efficiently. To reduce the interference of background in target localization, a corrected background-weighted histogram and background update mechanism are adapted to decrease the weights of both prominent background color and texture features similar to the target object. Comparative experimental results on various challenging videos demonstrate that the proposed tracker performs favorably against several variants of state-of-the-art mean shift tracker when heavy occlusions and complex background changes exist.

Unsupervised motion detection with background update and shadow suppression

An algorithm is developed to detect moving object and suppress shadow. According to motion variations caused by some moving objects in a scene, a background update approach is proposed. The developed update method efficiently prevents undesired corruption of background and does not consider the adaptation coefficient or the learning rate used in some existing algorithms. A multi-scale wavelet transform methodology is used to segment foreground from a clutter background. The optimal selection of threshold value is automatically determined which does not require any complex supervised training or manual calibration. According to photometric invariant, a color ratio difference is proposed to suppress shadow. Some complete foreground motion object regions are extracted by integrating moving object segmentation in the multi-scale wavelet with shadow suppression in the color ratio difference. The mentioned method is less affected by the presence of moving objects in a scene. Experimental results show that the proposed approach is efficient in detecting motion objects and suppressing shadows by comparisons.

Optimization of Image Processing in Video-based Traffic Monitoring

The video-based traffic monitoring systems have been widely used. The system usually reads real time monitoring video and converts it into images for processing. However, such systems are often limited by image processing algorithms and don’t behavior as well as expected. We hereby propose optimization approaches for of image processing. As in image processing, getting a binary image is usually a fundamental step, we first present an adaptive thresholds approach for binary conversion. The approach takes into consideration the space information of the pixel and chooses thresholds by adaptively according to each pixel and its neighboring pixels. Then we introduce a three-dimension Gaussian filter, which has best quantity-time tradeoff, to remove noise in the image. Although widely used, background subtraction is limited by background refreshing. We propose a generative model approach that is based on Gaussian model and Gaussian distribution, to generate background so that we can update background at any time. We also add in moving objects shadow detection and removing mechanism in moving objects segmentation. In real world monitoring, removing disturbs from burst noise is a hard problem. We, taking advantage of the characteristic that most of the burst noise is sudden and short-term, put forward a burst noise eliminating algorithm that uses several continuous frames to wipe off such sudden noise. Particularly, we studied the characteristics of H, S and V component of reflection light band, and assert that removal of the reflection light band is to eliminate negative effect from high-energy reflection light. We use Gaussian model and Sobel operator to achieve reflection light band removing; we also as utilize Canny algorithm to wipe off edge corrosion. Finally we achieved an integrated optimized solution on traffic monitoring system by making a tradeoff between time and effect. DOI: http://dx.doi.org/10.5755/j01.eee.18.8.2634

Background Extraction and Vehicle Detection Method Based on Histogram in YCbCr Color Space

In computer vision-based Intelligent Transportation Systems (ITS), one of the key techniques is to detect the vehicles accurately. In this paper, we propose a background extraction and vehicle detection method based on histogram in YCbCr color space. By using YCbCr color space, the influence of illumination change and shadows is reduced. To solve the problem with change in background itself, we propose a background update method by using the pixel change count and histogram. Experiment results show that the proposed algorithm can effectively extract and update the background information in complicated urban traffic environment. It also improves the accuracy of vehicle detection.