Dynamic Background Changes Research Articles

Background subtraction via regional multi-feature-frequency model in complex scenes

Although a wide variety of background subtraction methods has been proposed in recent years, none has been able to fully address multi-scale moving objects and dynamic background in real surveillance tasks. In this paper, a novel and effective background subtraction method, named regional multi-feature-frequency (RMFF), is proposed to detect multi-scale moving objects under dynamic background. Unlike many existing methods construct background model using simple multi-feature combinations, RMFF exploits the spatiotemporal cues of multi-feature as well as superpixels at each scale, thus allowing for more robust information to be exploited for background modeling. Specifically, the spatial relationship between pixels in a neighborhood and the frequencies of features over time are first exploited, enabling accurate detection of moving objects while ignoring most dynamic background changes. Then, the use of multi-scale superpixels for exploiting the structural information existing in real-world scenes further enhances robustness to multi-scale objects and environmental variations. Finally, an adaptive strategy is employed to dynamically adjust the foreground/background segmentation threshold for each region without user intervention. This adaptive threshold is defined for each region separately, and can adjust dynamically based on continuous monitoring of the background changes, thereby effectively reducing potential segmentation noise. Experiments on the 2014 version of the ChangeDetection.net dataset demonstrate that the proposed method outperforms the 12 state-of-the-art algorithms in terms of overall F-Measure and performs effectively in many complex scenes. Consequently, it is verified that the developed approach is feasible and useful for robust application in practical video surveillance.

Unsupervised moving object segmentation using background subtraction and optimal adversarial noise sample search

Moving Objects Segmentation (MOS) is a fundamental task in many computer vision applications such as human activity analysis, visual object tracking, content based video search, traffic monitoring, surveillance, and security. MOS becomes challenging due to abrupt illumination variations, dynamic backgrounds, camouflage and scenes with bootstrapping. To address these challenges we propose a MOS algorithm exploiting multiple adversarial regularizations including conventional as well as least squares losses. More specifically, our model is trained on scene background images with the help of cross-entropy loss, least squares adversarial loss and ℓ1 loss in image space working jointly to learn the dynamic background changes. During testing, our proposed method aims to generate test image background scenes by searching optimal noise samples using joint minimization of ℓ1 loss in image space, ℓ1 loss in feature space, and discriminator least squares loss. These loss functions force the generator to synthesize dynamic backgrounds similar to the test sequences which upon subtraction results in moving objects segmentation. Experimental evaluations on five benchmark datasets have shown excellent performance of the proposed algorithm compared to the twenty one existing state-of-the-art methods.

A Novel Decomposition Model for Visual Rail Surface Inspection

Rail surface inspection plays a pivotal role in large-scale railway construction and development. However, accurately identifying possible defects involving a large variety of visual appearances and their dynamic illuminations remains challenging. In this paper, we fully explore and use the essential attributes of our defect structure data and the inherent temporal and spatial characteristics of the track to establish a general theoretical framework for practical applications. As such, our framework can overcome the bottleneck associated with machine vision inspection technology in complex rail environments. In particular, we consider a differential regular term for background rather than a traditional low-rank constraint to ensure that the model can tolerate dynamic background changes without losing sensitivity when detecting defects. To better capture the compactness and completeness of a defect, we introduce a tree-shaped hierarchical structure of sparse induction norms to encode the spatial structure of the defect area. The proposed model is evaluated with respect to two newly released Type-I/II rail surfaces discrete defects (RSDD) data sets and a practical rail line. Qualitative and quantitative evaluations show that the decomposition model can handle the dynamics of the track surface well and that the model can be used for structural detection of the defect area.

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.

Background Subtraction Based on a New Fuzzy Mixture of Gaussians for Moving Object Detection

Moving foreground detection is a very important step for many applications such as human behavior analysis for visual surveillance, model-based action recognition, road traffic monitoring, etc. Background subtraction is a very popular approach, but it is difficult to apply given that it must overcome many obstacles, such as dynamic background changes, lighting variations, occlusions, and so on. In the presented work, we focus on this problem (foreground/background segmentation), using a type-2 fuzzy modeling to manage the uncertainty of the video process and of the data. The proposed method models the state of each pixel using an imprecise and adjustable Gaussian mixture model, which is exploited by several fuzzy classifiers to ultimately estimate the pixel class for each frame. More precisely, this decision not only takes into account the history of its evolution, but also its spatial neighborhood and its possible displacements in the previous frames. Then we compare the proposed method with other close methods, including methods based on a Gaussian mixture model or on fuzzy sets. This comparison will allow us to assess our method’s performance, and to propose some perspectives to this work.

Twin background model for foreground detection in video sequence

The realm of video surveillance has various methods to extract foreground. Background subtraction is one of the prime methods for automatic video analysis. The sensitivity of a meaningful event of interest is increased due to dampening effect of background changes and detection of false alarm. Hence the model can be strongly recommended to industries. This paper restricts the focus to one of the most common causes of dynamic background changes: that of swaying trees branches and illumination changes. To overcome the issue available in existing system, we propose a method called as twin background modeling. This method has dual models namely long and short term background models to increase exposure rate of foreground by using statistical method and also reduce false negative rate. This method has dimensional transformation from 2D to 1D which reduces computation time of the system and increases batch processing. The proposed method uses Manhattan distance to reduce execution time, increase detection rate and reduce error rate. The performance of the suggested approach is illustrated by using change detection dataset 2014 and is compared to other conventional approaches.

A Drastic Change in Background Luminance or Motion Degrades the Preview Benefit.

When some distractors (old items) precede some others (new items) in an inefficient visual search task, the search is restricted to new items, and yields a phenomenon termed the preview benefit. It has recently been demonstrated that, in this preview search task, the onset of repetitive changes in the background disrupts the preview benefit, whereas a single transient change in the background does not. In the present study, we explored this effect with dynamic background changes occurring in the context of realistic scenes, to examine the robustness and usefulness of visual marking. We examined whether preview benefit in a preview search task survived through task-irrelevant changes in the scene, namely a luminance change and the initiation of coherent motion, both occurring in the background. Luminance change of the background disrupted preview benefit if it was synchronized with the onset of the search display. Furthermore, although the presence of coherent background motion per se did not affect preview benefit, its synchronized initiation with the onset of the search display did disrupt preview benefit if the motion speed was sufficiently high. These results suggest that visual marking can be destroyed by a transient event in the scene if that event is sufficiently drastic.

Object Detection and Tracking in Discriminant Subspace

<div><p>Detection and tracking of moving objects in video is essential for many computer vision applications and it is considered as a challenging research issue due to dynamic changes in background, illumination, object size and shape. Many traditional algorithms fails to detect and track the moving objects accurately, this paper proposes a robust method, to detect and track moving objects based on the combination of background subtraction and Orthogonalized Fisher’s Discriminant (OFD). Background subtraction detects the foreground objects on subtracting frame by frame basis and updating the background model recursively. Orthogonalized Fisher’s Discriminant projects high dimensional data onto a one dimensional space with the highest recognizability, which speedup the detection and tracking process and also preserves the structure of the objects resulting high accuracy. The proposed method is tested on standard datasets with complex environments and experimental results obtained are encouraging.</p></div>

A framework for moving object segmentation using dynamic background modelling and shadow suppression in complex wavelet domain

In this paper, a framework for dynamic background modelling and shadow suppression under rapidly changing illumination conditions for moving object segmentation in complex wavelet domain is proposed which deals with the problems of ghosts, object shadows, noise, object distortion in dynamic background changes. The proposed method consists of eight steps applied on given video frames which include: wavelet de-composition of frame using complex wavelet transform; use of change detection on detail coefficients; use of dynamic background modelling on approximate co-efficient; cast shadow suppression; use of soft thresholding for noise removal; strong edge detection; inverse wavelet transformation for reconstruction and finally using closing morphology operator. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for four datasets in terms of various performance measures. Experimental results demonstrate the efficacy of the proposed method is better in terms of relative foreground area measure, misclassification penalty, relative position based measure, normalised cross-correlation, normalised absolute error, peak signal-to-noise ratio and pixel classification based measure as compared to other standard methods.

Framework for dynamic background modeling and shadow suppression for moving object segmentation in complex wavelet domain

Moving object segmentation using change detection in wavelet domain under continuous variations of lighting condition is a challenging problem in video surveillance systems. There are several methods proposed in the literature for change detection in wavelet domain for moving object segmentation having static backgrounds, but it has not been addressed effectively for dynamic background changes. The methods proposed in the literature suffer from various problems, such as ghostlike appearance, object shadows, and noise. To deal with these issues, a framework for dynamic background modeling and shadow suppression under rapidly changing illumination conditions for moving object segmentation in complex wavelet domain is proposed. The proposed method consists of eight steps applied on given video frames, which include wavelet decomposition of frame using complex wavelet transform; use of change detection on detail coefficients (LH, HL, and HH), use of improved Gaussian mixture-based dynamic background modeling on approximate coefficient (LL subband); cast shadow suppression; use of soft thresholding for noise removal; strong edge detection; inverse wavelet transformation for reconstruction; and finally using closing morphology operator. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for six datasets in terms of various performance measures. Experimental results demonstrate the efficacy of the proposed method.

Detection of dynamic background due to swaying movements from motion features.

Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: 1) that of swaying tree branches and 2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

Study on adaptive ability of Gaussian mixture background model

Gaussian mixture background model is an online parameterized statistical model,and the presenting way of pixel sample pattern observed from time window has great influence on the model's learning result.According to the characteristics of dynamic background changes,issues such as the stability and plasticity of modal learning,the modal residual and activation,which affected the model's adaptive ability,were studied.The simulation results show that Gaussian mixture background model has a robust selective adaptability to gradual change,but a limited adaptability to transient variation of background configuration provided by modal residual and activation mechanism.