Moving Object Detection Algorithm Based on Gaussian Mixture Model and HSV Space

We propose two new methods in the nonlinear kernel feature space for pixel clustering based on the traditional KMeans and Gaussian mixture model (GMM). Unlike the previous work on the kernel machines, we give out a new perspective on the new developed kernel machines. That is, kernel principle component analysis (KPCA) combined with the KMeans and the GMM are kernel KMeans (KKMeans) and kernel GMM (KGMM), respectively. In this paper, we prove the new perspective on KKMeans and give out a clear statement on the KGMM as well. Based on this new perspectives, we can implement the KKMeans and the KGMM conveniently. At the end of the paper, we utilize these new algorithms on the problem of the colour image segmentation. Based on a series of experimental results on Corel colour images, we find that the KKMeans and KGMM can outperform the traditional KMeans and GMM consistently, respectively.

The accurate modeling of uncertain variables, such as wind power and load uncertainty, is an essential foundation for the planning operation and scheduling decisions in power systems. Currently, Gaussian mixture model (GMM) and Dirichlet process mixture model (DPMM) are the primary techniques used to characterize uncertainties in power systems, which are commonly solved by expectation-maximization (EM) algorithm. However, for the massive data of uncertain variables, the algorithm encounters challenges in accurately obtaining GMM and DPMM with a lower time consumption. To address this issue, we propose a method for GMM uncertainty modeling in power systems considering the mutual assistance of latent variables. Specifically, the GMM of uncertain variables is first constructed, and the conditional probability is employed to characterize the mutual assistance of latent variables. Then, an improved EM algorithm is developed to obtain the optimal GMM parameters, in which the expectation step (E-step) and maximization step (M-step) of the algorithm are revised using the conditional probability. Importantly, the closed-form solutions for GMM parameters are rederived in the revised E-step and M-step. Finally, the proposed uncertainty modeling method is compared with the traditional GMM and DPMM on actual wind power and load data from Australia. The proposed method performs the efficiency and accuracy in characterizing uncertainty.

Moving object detection is vitally used in video surveillance applications. Traditional Gaussian mixture model (GMM) based background subtraction (BGS) methods are usually performs well when background is stationary. However, they require parameter tuning to deal with dynamic backgrounds, whose background pixel values change over time. Particularly, the threshold which determines the pixels associated with moving objects from the resultant of BGS. To tackle this problem there is no ultimate solution. Considering that, this paper intents to present a novel idea to update the threshold of GMM based BGS with respect to color distortion, similarity and illumination measures in pixel level. Extensive experiments were carried out to demonstrate the effectiveness of the proposed method in comparison to some of the long-familiar GMM based BGS methods in literature. However, note that this paper is not attempted to provide a real-time technique, but rather to investigate the potential utilization of the aforementioned measures to set a threshold automatically to detect moving objects in video sequences.

in order to solve the adverse effects of strong light and shadow on the test results, a fusion frame difference and background subtraction method in the HSV space is used in this paper. By using frame difference method to solve the effect of strong light, but frame difference method can not detect object when the object do not move, the method of background subtraction can detect it, building Gaussian background model in the HSV space can eliminate shadows. Empirical results show that the method of fusion frame difference and background subtraction in the HSV space can get overcome the effect of strong light and shadows. Fusion background subtraction and frame difference method based on establishing a Gaussian mixture model in HSV space can overcome the disadvantages of the frame difference method, at the same time it can also solve the false detection of object which result from the background subtraction method.

For TV and radio shows containing narrowband speech, Speech-to-text (STT) accuracy on the narrowband audio can be improved by using an acoustic model trained on acoustically matched data. To selectively apply it, one must first be able to accurately detect which audio segments are narrowband. The present paper explores two different bandwidth classification approaches: a traditional Gaussian mixture model (GMM) approach and a spline-based classifier that categorizes audio segments based on their power spectra. We focus on shows found in the DARPA GALE Mandarin training and test sets, where the ratio of wideband to narrowband shows is very large. In this setting, the spline-based classifier reduces the number of misclassified wideband segments by up to 95% relative to the GMM-based classifier for the same number of misclassified narrowband segments.

In this work we present and apply a new Hyperspectral image classification method based on Dirichlet Process Gaussian mixture models (also known as infinite Gaussian mixture models - IGMMs). Since this approach is a non-parametric Bayesian method, we circumvent the problem of model selection which is unavoidable and often difficult when employing traditional parametric Gaussian mixture models (GMM). In order to infer model parameters from observations, we adopt a Gibbs Sampling method to sample the posterior distributions of these parameters. In the preprocessing step, we use Local Fisher's Discriminant Analysis (LFDA) for dimension reduction since we expect it to preserve the multi-modal, non-Gaussian structure of the hyperspectral data. We compared our proposed IGMM based classification method to the existing state-of-the-art classification methods using popular hyperspectral imagery datasets. To simulate challenging real-world sensing environment, we mix every pixel with background and study the performance of these classification approaches. Our experiments show that the proposed LFDA-IGMM method and GMM method have almost the same performance for most cases, given “pure” data - with mixed pixels, LFDA-IGMM outperformed GMM for sub-pixel classification. Both methods outperformed the other commonly used classification approaches when the training sample size is relatively large. Also, the IGMM based method is less sensitive to pixel mixing than other methods.

The Gaussian mixture model (GMM) is an important background model of background subtraction methods in moving object detection, and is fit to deal with gradual changes of illumination. To handle sharp changes, hierarchical GMM (HGMM) is proposed as a generic solution which uses state models without temporal correlation on different scales. A new on-line EM algorithm is devised to model new states quickly and accurately. Experiments show that the presented method brings fast adaptation to sharp changes of illumination.

To make the supervector modeling of speech utterance more effective and accurate, this paper proposes a new duration weighted Gaussian Mixture Model (GMM) supervector modeling method for robust speaker recognition. At the beginning, this method adapts the acoustic features of speech utterance to GMM from a common basic Universal Background Model (UBM) with Maximum A Posterior (MAP) criterion and then models GMM supervector by bounding the Kullback-Leibler (KL) divergence measure. In addition, a duration weight supervector is modeled for using duration information of speech utterances. Furthermore, this paper presents a method of how to effectively apply them together during training and classification. Experimental results on American National Institute of Standards and Technology Speaker Recognition Evaluation (NIST SRE) 2008 dataset demonstrate that the proposed method outperforms the traditional GMM supervector modeling with relative 16% and 10% improvements of Equal Error Rate (EER) and Minimum Detection Cost Function (MinDCF), respectively.

Object detection is an important task for computer vision applications. Many researchers have proposed a number of methods to detect the objects through background modeling. To adapt to “illumination changes” in the background, local feature-based background models are proposed. They assume that local features are not affected by background changes. However, “motion changes”, such as the movement of trees, affect the local features in the background significantly. Therefore, it is difficult for local feature-based models to handle motion changes in the background. To solve this problem, we propose a new background model in this paper by applying a statistical framework to a local feature-based approach. Our proposed method combines the concepts of statistical and local feature-based approaches into a single framework. In particular, we use illumination invariant local features and describe their distribution by Gaussian Mixture Models (GMMs). The local feature has the ability to tolerate the effects of “illumination changes”, and the GMM can learn the variety of “motion changes”. As a result, this method can handle both background changes. Some experimental results show that the proposed method can detect the foreground objects robustly against both illumination changes and motion changes in the background.

We introduce an optimal mass transport framework on the space of Gaussian mixture models. These models are widely used in statistical inference. Specifically, we treat Gaussian mixture models as a submanifold of probability densities equipped with the Wasserstein metric. The topology induced by optimal transport is highly desirable and natural because, in contrast to total variation and other metrics, the Wasserstein metric is weakly continuous (i.e., convergence is equivalent to convergence of moments). Thus, our approach provides natural ways to compare, interpolate and average Gaussian mixture models. Moreover, the approach has low computational complexity. Different aspects of the framework are discussed and examples are presented for illustration purposes.

This work proposes a fast background learning algorithm for foreground detection under changing illumination. Gaussian Mixture Model (GMM) is an effective statistical model in background learning. We first focus on Titterington's online EM algorithm that can be used for real-time unsupervised GMM learning, and then advocate a deterministic data assignment strategy to avoid Bayesian computation. The color of the foreground is apt to be influenced by the environmental illumination that usually produce undesirable effect for GMM updating, however, a collinear feature of pixel intensity under changing light is discovered in RGB color space. This feature is afterward used as a reliable clue to decide which part of mixture to update under changing light. A foreground detection step proposed in early version of this work is employed to extract foreground objects by comparing the estimated background model with the current video frame. Experiments have shown the proposed method is able to achieve satisfactory static background images of scenes as well as is also superior to some mainstream methods in detection performance under both indoor and outdoor scenes.

Gaussian mixture model (GMM) is one of the best models for modeling a background scene with gradual changes and repetitive motions. However, it fails in segmenting moving objects when the scene changes sharply. To handle this problem, a novel background modeling algorithm - memorizing GMM is proposed, which is inspired by the way human perceive the environment. It can make the GMM remember what the scene has ever been during the learning and updating period. Experimental results show that it can help segmenting moving objects precisely when the scene changes sharply.

Image segmentation has always been an important research direction in the field of images processing, however, due to the long cycle of algorithm, the image segmentation techniques have never been widely applied. According to the problem above, a image segmentation algorithm of Gaussian Mixture Model (GMM) based on Map/Reduce is proposed to improve the real-time performance. Firstly, the architecture of Hadoop image processing interface (Hipi) is presented to improve the ability of Map/Reduce to process small image. The K-means is present to generate the init value of Gaussian Mixture Model to avoid local optimal. Secondly, the cloud computational model of Map/Reduce is proposed to realize the parallel of GMM, and the fusion method of the sub-model for GMM is presented to keep the accuracy of segmentation. Finally, the experiment is carried out based on the cloud platform of aliyun. Results show the image segmentation algorithm of GMM based on Map/Reduce can greatly improve the real-time performance with keeping the accuracy of segmentation.

This paper presents a novel approach for background/foreground segmentation of RGBD data with the Gaussian Mixture Models (GMM). We first start by the background subtraction from the colour and depth images separately. The foregrounds resulting from both streams are then fused for a more accurate detection. Our segmentation solution is implemented on the GPU. Thus, it works at the full frame rate of the sensor (30fps). Test results show its robustness against illumination change, shadows and reflections.

Gaussian mixture model (GMM) is one of the best models for modeling a background scene with gradual changes and repetitive motions. However,it fails when the scene changes suddenly,e.g.,a door is opened or closed. To handle such problems,we propose a memory-based Gaussian mixture model (MGMM) inspired by the way human perceives the environment. The human memory mechanism is introduced to model the background. Each pixel of every frame is processed and transferred through three spaces:ultra-short time memory space,short time memory space,and long time memory space. The proposed memory-based model can remember what the scene has ever been,which helps the model adapt to the variation of the scene more quickly.