Efficient method for moving object detection in cluttered background using Gaussian Mixture Model
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Foreground object detection in video is a fundamental step for automated video surveillance system and many computer vision applications. Mostly moving foreground object is detected by background subtraction techniques. In dynamic background, Gaussian Mixture Model (GMM) performs better for object detection. In this work, a GMM based Basic Background Subtraction (BBS) model is used for background modeling. The connected component and blob labeling has been used to improve the model with a threshold. Morphological operators are used to improve the foreground information with a suitable structure element. The experimental study shows that the proposed work performs better in comparison to considered state-of-the-art methods in term of error.Keywords:
Foreground detection
<p>Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation.</p>
Motion Detection
Subtraction
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For the multi-object detection problems with complex background, a method which combines background subtraction with Guassian pyramid on objects detection is presented.The method detects the whole object with taking samples from the objects with Guassian pyramid, building background models, extracting foreground areas from background subtraction, and eliminating the shadow on the foreground.The detection that integrates Gauss model which concerns the background renewal of calculation, overcomes the error resulted from the sudden change of background.A dynamic threshold concept is proposed to enhance detection effect, thus it increases the possibility of implementation.The experiment results show that moving objects detected by the proposed method is more reliable than the state-of-the-art.
Pyramid (geometry)
Foreground detection
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Subtraction
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Background subtraction is a real time effective technique for detecting moving foreground objects in image sequences from a static camera. Background modelling plays an important role in this technique of foreground object detection. Active real time background modelling in presence of moving foreground objects in the scene and adaption of background model to gradual changes due to gradual illumination changes and addition of new immoveable objects into the scene are addressed in this paper. We present a queue based algorithm for real time, active, and adaptive background modelling. Segmentation of the foreground and robust detection of shadow is performed via comparison with background statistics in YCrCb color space. The problem of a single foreground object splitting into two or more segments due to similarity of foreground pixel color with the background in most cases can be ameliorated with the use of a fast single pass the hysteresis thresholding technique. We demonstrate various results of background modelling, segmentation and shadow detection results for both indoor and outdoor scenes.
Foreground detection
Similarity (geometry)
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Background subtraction is one of the main techniques to extract moving objects from background scenes. A mixture of Gaussians is a common model for background subtraction that has been used in many applications. However modelling background pixels using this model results into a low-level process at pixel level. Some of its main drawbacks are: a subtracted (moving object) region may contain holes; it cannot solve partial occlusion problems, and it requires updates in cases of shadows or sudden changes in the scene. We present a multi-layered mixture of Gaussians model named PixelMap. We combine the mixture of Gaussians model with concepts defined by region level and frame level considerations. Our experimental results show that our method improved the accuracy of extracting moving objects from background. A single stationary camera has been used.
Background image
Foreground detection
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Background subtraction is an essential technique for moving object segmentation in vision surveillance system. To acquire an exact background, Gaussian mixture modeling (GMM) is a popular method for its adaptation to background variations. However, limited training samples and complex scenes result in heavy tails for GMM, which significantly affect the moving object detection accuracy. By reviewing the formulations of GMM, we construct a student's t-distribution mixture background model (SMBM) on the basis of fuzzy c-means clustering partition algorithm. Then, we present a method for moving object segmentation based on confidence analysis. Experimental results show that the background model can reflect complex scenes; our method achieves efficient object detection than conventional GMM approaches.
Foreground detection
Background image
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In previous surveillance applications, algorithms for background modeling based on Gaussian mixture models (GMM) needed to specify two parameters: threshold T, which determines a proportion of the data that should be accounted for by the background, and a learning rate alpha specifying speed at which the distribution parameters change [Stauffer, CVPR 1999}. In the Basic Background Subtraction (BBS), foreground objects are found by subtracting a static foreground image. In the proposed algorithm, BBS is applied using background obtained from GMM. This way, threshold T is replaced by a foreground-background separation threshold S. The advantage is that S is less sensitive than T. To make the model respond faster to changes, recent observed value of the most dominant background component is used as a current value for a particular pixel, rather than the component mean value. Quantitative and qualitative results show the advantages of the proposed technique compared to GMM models.
Component (thermodynamics)
Foreground detection
Value (mathematics)
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In several video surveillance applications, such as the detection of abandoned/stolen objects or parked vehicles,the detection of stationary foreground objects is a critical task. In the literature, many algorithms have been proposed that deal with the detection of stationary foreground objects, the majority of them based on background subtraction techniques. In this paper we discuss various stationary object detection approaches comparing them in typical surveillance scenarios (extracted from standard datasets). Firstly, the existing approaches based on background-subtraction are organized into categories. Then, a representative technique of each category is selected and described. Finally, a comparative evaluation using objective and subjective criteria is performed on video surveillance sequences selected from the PETS 2006 and i-LIDS for AVSS 2007 datasets, analyzing the advantages and drawbacks of each selected approach.
Foreground detection
Subtraction
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Background subtraction is one of the main techniques to extract moving objects from background scenes. A mixture of Gaussians is a common model for background subtraction that has been used in many applications. However modelling background pixels using this model results into a low-level process at pixel level. Some of its main drawbacks are: a subtracted (moving object) region may contain holes; it cannot solve partial occlusion problems, and it requires updates in cases of shadows or sudden changes in the scene. We present a multi-layered mixture of Gaussians model named PixelMap. We combine the mixture of Gaussians model with concepts defined by region level and frame level considerations. Our experimental results show that our method improved the accuracy of extracting moving objects from background. A single stationary camera has been used.
Foreground detection
Background image
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Background subtraction is one of the main techniques to extract moving objects from background scenes. A mixture of Gaussians is a common model for background subtraction. There are several parameters involved in such a model. Obviously, the assignment of initial values to these parameters affects the accuracy of background subtraction. In this paper, we analyze in detail the impact of different initial parameter values based on our model implementation. Both indoor and outdoor video sequences have been tested. This parameter value analysis provides suggestions how to choose suitable initial parameter values, assign reasonable thresholds which ensure better results, while using a mixture of Gaussians model in video surveillance applications.
Subtraction
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