Block Background Subtraction Method Based on ViBe

In this paper, we introduce a new method to reliably ex- tract humans from a video sequence even when the humans are static for long periods of time. The proposed method addresses a common problem in background subtraction techniques whereby humans that are static are mistaken for new additions to the background scene and are consequently absorbed into the background model. In the proposed method, codebook background subtraction is used to identify foreground regions in the video frame. A motion based particle filter is then used to track one or more human heads in the frame and determine which of these foreground regions represent people. The background model is then selectively updated given this knowledge thus ensuring that people will never be absorbed into the background model once detected, even when indefinitely static. Simulation results confirm that a human body is robustly extracted using this method in a non-static environment. (5 pages)

Background subtraction in real applications: Challenges, current models and future directions

The problem of foreground detection in real‐time video surveillance applications is addressed. Proposes is a framework, which is computationally cheap and has low memory requirements. It combines two simple processing blocks, both of which are essentially background subtraction algorithms. The main novelty of the approach is a combination of an autoregressive moving average filter with two background models having different adaptation speeds. The first model, having a lower adaptation speed, models long‐term background and detects foreground objects by finding areas in the current frame which significantly differ from the proposed background model. The second model, with a higher adaptation speed, models the short‐term background and is responsible for finding regions in the scene with a high foreground object activity. The final foreground detection is built by combining the outputs from these building blocks. The foreground obtained by the long‐term modelling block is verified by the output of the short‐term modelling block, i.e. only the objects exhibiting significant motion are detected as real foreground objects. The proposed method results in a very good foreground detection performance at a low computational cost.

On the role and the importance of features for background modeling and foreground detection

Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient, and Haar-like features are integrated to handle spatio-temporal variations for each pixel. A pixelwise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. We compare the performance of the algorithm with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively.

This paper proposes a moving target detection scheme suitable for camera motion. Firstly, the background model is initialized by a Gaussian mixture model algorithm. Then Kanade-Lucas-Tomasi Feature Tracker (KLT) method is used to detect optical flow feature points of two adjacent frames, RANdom SAmple Consensus (RANSAC) algorithm is used to filter out the correct matching points and obtain a homography matrix, which can recover the background model matching the current frame, the new background model is used to detect moving target of the current frame. In the foreground detection stage, the current pixel is first compared with its own background model, and then compared with the background model of its 8 neighborhood pixels, the algorithm is speeded up without reducing the detection accuracy in this way; In the update stage of the background model, in order to adapt to the background changes caused by camera motion, an age value variable is set for each pixel. The experimental results show that the improved algorithm has a significant improvement in detection accuracy and running time compared to Gaussian mixture background modeling.

In this paper, we propose a novel background subtraction method that makes use of spectral, spatial, and temporal features extracted from the video sequence in determination of the best background candidates for background modeling. As the final step of our process, the binary moving object detection mask is computed using prompt background subtraction with our proposed background model. The overall results of these analyses thus demonstrate that our proposed method substantially outperforms existing methods by an F1 metric accuracy rate increase of up to 79%.

Background subtraction method is widely used in most of the video motion detection algorithms especially for video surveillance application. Background subtraction is used to extract moving or static foreground objects from the background scene. The efficiency of foreground-background segmentation heavily relies on background model which must be able to cope with changes in the scene and granularity of the foreground objects. The robust background model can produce good foreground segmentation results and it is still a great challenge to get accurate and high performance result today. In this paper, a video foreground-background segmentation approach is proposed. This approach is based on Codebook (CB) model with Kalman Filter. This approach can be used to extract foreground objects from the video stream. The Lab color space is used in this approach to calculate color difference between two pixels using CIEDE2000 color difference formula. Extracted foreground object from video sequence using this approach is useful for object detection in video surveillance applications. This approach has been tested with PETS and CDnet2014 datasets and segmentation results accuracy are evaluated compare with ground truth.

Moving Object Detection systems have long been in use to look at security sensitive areas. The manufacture of video investigating systems sensible desires fast, reliable and durable algorithms for moving object detection classification pursuit and activity analysis. Moving object detection is that the essential step for extra analysis of video. It handles segmentation of moving objects from stationary background objects. Object classification step categorizes detected objects into preened classes like human, vehicle, animal, clutter, etc. it's a necessity to inform apart objects from each other therefore on trace and analyse their actions reliably. In previous system we have got performed background subtraction by victimization clever Edge Detection. In clever Edge Detection technique we tend to tend to square measure taking 2 photos for comparison those square measure background image and foreground image. Previous strategies for object detection square measure methodology along side object detectors image segmentation and Background subtraction. Our methodology aims to section objects supported motion info and it contains a part of background modelling. In the previous strategies we tend to tend to square measure conducting background subtraction only for photos. For this we tend to tend to projected an image component wise background modelling and subtraction technique mistreatment multiple choices. Hence, throughout this color, gradient and Hear-Like choices square measure integrated to handle the variation element. Thus, motion segmentation and background subtraction square measure the foremost connected topics to the current paper.

Detection of video objects under bad weather and poor illumination condition is a challenging task. We address this issue using the notion of background modeling. LBP-based background modeling and model learning has been used to detect the video objects but performance degrades in the above complex background. We propose the adjacency- and reinforced adjacency-based variants of LBP for complex real-world background modeling and model learning for object detection. In this regard, we have proposed the four variants; (1) enhanced adjacent local binary pattern, (2) enhanced reinforced adjacent local binary pattern, (3) enhanced adjacent local ternary pattern, and (4) enhanced reinforced adjacent local ternary pattern. Besides, we have embedded the Gabor and LBP features to obtain an embedded feature, which is subsequently used with the notions of adjacency. Unlike the background learning approach where the model learns only the background, our model learning algorithm learns the background together with the foreground objects and hence named as mixed-mode learning strategy. These models together with the new learning strategy are tested with CD2014 (snowfall and blizzard) and PETS (2014 and 2016) data sets, and the performance of the proposed models has been compared with LBP-based methods and other state-of-the-art methods.

This paper presents a hybrid pixel-based background (HPB) model, which is constructed by single stable record and multi-layer astable records after initial learning. The image foreground object detection must face the problems of moving background, illumination changes, chaotic, etc. in real word applications. In our approach, the HPB model can be used for background subtraction to extract objects precisely in various complex scenes. Using the multi-layer astable records, we also propose the homogeneous background subtraction that can detect the foreground object with less record memory. Based on the benchmark videos, the experimental results show that single stable and 3-layer multi-layer astable records can be enough for background model construction and then updated quickly to overcome the background variation. The proposed approach can improve the averages Error Rate of foreground object detection up to 86% when comparing with the latest works.

Moving object detection is the basic of video applications such as computer vision, object recognition and tracking, surveillance security etc. Background subtraction and symmetrical differencing are the popular methods of motion detection. The main idea of them is to compare the current video frame with a specified background image or a background model or the next video frame. For background subtraction, the obtaining of initialization is crucial and many methods have been employed, so it is necessary to model background to adapt the changes of background. In this paper, the single gaussian modeling as the initialization background model combined with an improved linear alternate background updating method is proposed. And then, a novel moving human detection method which employs background subtraction and symmetrical differencing based on rgb color difference model is presented. The experimental results show that the detection method can detect moving human effectively and real-time.

Moving shadows cause serious problem while segmenting and extracting foreground from video sequences, due to the misclassification of moving shadow as foreground. In order to detect the moving objects accurately, a method based on the similarity between little textured patches was proposed to remove the moving shadows. Firstly, the potential shadows were detected by analyzing the intensity and color properties. Secondly, the shadow detection approach was improved by evaluating the textural similarity between the current frame and the corresponding background model. Finally, the geometric heuristics were imposed to further improve the performance. Experimental results on both indoor and outdoor scenes exhibit that the proposed method succeeds in removing shadows robustly and achieves real-time performance.

Although background modeling and foreground detection are not mandatory steps for computer vision applications, they may prove useful as they separate the primal objects usually called ”foreground” from the remaining part of the scene called ”background”, and permits different algorithmic treatment in the video processing field such as video-surveillance, optical motion capture, multimedia applications, teleconferencing and human-computer interfaces. Conventional background modeling methods exploit the temporal variation of each pixel to model the background and the foreground detection is made by using change detection. The last decade witnessed very significant publications on background modeling but recently new applications in which background is not static, such as recordings taken from mobile devices or Internet videos, need new developments to detect robustly moving objects in challenging environments. Thus, effective methods for robustness to deal both with dynamic backgrounds, illumination

Foreground detection is one of the active research topics in computer vision and a precondition for intelligent video analytics. Background subtraction is often considered as a reliable method for extracting foreground from the video sequences. The real-world challenges such as intermittent object motion, dynamic backgrounds, bad weather conditions, shadows, and illumination variations affect the performance of foreground detection. Most of the conventional background subtraction methods result in faulty detection and fragmented foreground in case of complex scenes. In recent years, deep neural networks make revolutionary advances in the area of foreground detection. The convolutional neural networks based background subtraction methods are notable for their performance but their performance drop in case of unseen videos and incur high-computational cost. In this paper, we investigate the fusion of superpixel with an adaptive background modeling for foreground detection in several challenging videos. A non-iterative clustering method is adopted for superpixel clustering because of its computational efficiency. Integration of effective superpixel algorithm with background subtraction has paved the way for improvements in unsupervised background subtraction methods. The proposed method is evaluated on different videos of changedetection.net 2014 dataset in order to validate its effectiveness on critical challenges of background subtraction.KeywordsBackground modelingForegroundMoving objectsSuperpixelsUnsupervised