A unified threshold updating strategy for multivariate Gaussian mixture based moving object detection

Moving object detection is an important and basic step in any video surveillance application. Detected object has used an input to higher level tasks such as tracking, event detection, and behavioral analysis. There are several challenges in moving object detection such as illumination changes, occlusion, shadow, bootstrap, dynamic background etc. Detection of moving object having similar color as background structure is called as camouflage, which is another big challenge. The method of detecting camouflage moving object is known as Decamouflaging. In recent years, the researchers are attracted towards the camouflage moving object detection due to its application in the military. In this review paper, different techniques using various features for camouflaged moving obj ect detection were studied.

Moving object detection is one of the most important tasks in intelligent visual surveillance systems. Gaussian Mixture Model (GMM) has been most widely used for moving object detection, because of its robustness to variable scenes. However, to the best of our knowledge, existing GMM based methods can not detect moving objects which gradually stop and keep still state for a while. In this paper, we present an Enhanced Gaussian MixtureModel, called EGMM, to handle this problem. We integrate an Initial Gaussian Background Model (IGBM) and an extended Kalman filter based tracker with GMM, to enhance its performance. Experimental results show that our EGMM based method has a lower miss rate at the same false positives per image comparing to GMM based method for moving pedestrian detection, and it also has a higher detection rate for abandoned object detection comparing to GMM based method.

In recent years, the numbers of Visual Surveillance systems have greatly increased, and these systems have developed into intellectual systems that automatically detect, track, and recognize objects in video. Automatic moving object detection and tracking is a very challenging task in video surveillance applications. In this regard, many methods have been proposed for Moving Object Detection and Tracking based on edge, color, texture information. Due to unpredictable characteristics of objects in foggy videos, the task of object detection remains a challenging problem. In this paper, we propose a novel scheme for moving object detection based on Log Gabor filter (LGF) and Dominant Eigen Map (DEM) approaches. Location of the moving object is obtained by performing connected component analysis. In turn, a Moving Object is Tracked based on the centroid manipulation. Number of experiments is performed using indoor and outdoor video sequences. The proposed method is tested on standard PETS datasets and many real time video sequences. Results obtained are satisfactory and are compared with existing well known traditional methods.

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 in the presence of complex dynamic backgrounds such as swaying of trees, spouting of water from fountain, ripples in water, flag fluttering in the wind, camera jitters, noise, etc., is known to be very difficult and challenging task. In addition to this, illumination variation, camouflage and real-time constraint aggravate the problem further. Background subtraction (BS) is a widely used algorithm for moving object detection in the presence of static cameras. Its performance purely depends on the choice of features used for background modeling. In this paper, we have proposed a novel multi-feature and multi-modal based background subtraction using Local Binary Pattern (LBP) and Histogram of oriented Gradients (HOG) for complex dynamic scene. Each pixel is modeled as a set of multi-feature calculated from its neighborhood and multi-modal BS is performed using Gaussian mixture model (GMM). To show its efficacy, the proposed algorithm is compared with some of the state-of-the-art BS techniques. In order to evaluate the algorithm in uncontrolled environments, a collection of publicly available database has been used. Quantitative and qualitative results justify our algorithm for efficient moving object detection in the presence of swaying of trees, camouflage and ripples in the water surface.

Moving object detection in urban scenes is important for the guidance of autonomous vehicles, robot navigation, and monitoring. In this paper moving objects are automatically detected using three sequential frames and tracked over a longer period. To this extend we modify the plane+parallax, fundamental matrix, and trifocal tensor algorithms to operate on three sequential frames automatically, and test their ability to detect moving objects in challenging urban scenes. Frame-to-frame correspondences are established with the use of SIFT keys. The keys that are consistently matched over three frames are used by the algorithms to distinguish between static objects and moving objects. The tracking of keys for the detected moving objects increases their reliability over time, which is quantified by our results. To evaluate the three different algorithms, we manually segment the moving objects in real world data and report the fraction of true positives versus false positives. Results show that the plane+parallax method performs very well on our datasets and we prove that our modification to this method outperforms the original method. The proposed combination of the advanced plane+parallax method with the trifocal tensor method improves on the moving object detection and their tracking for one of the four video sequences.

Moving object detection and feature extraction algorithm in video sequences are discussed in this paper. There are several problems in moving object detection and extraction from outdoor video surveillance, that is, moving object detection algorithm is easily interfered by background of video monitor, the feature of moving object is difficult to extract from video source, and the vibration of picture frame in outdoor video surveillance causing by wind factors effects the incorrect extraction of the moving object. The vibration causing by wind factors was corrected and an enhancement inter-frame difference algorithm based on difference histogram threshold selection is presented in this paper. The experiment results prove that this method can detect and extract the moving object accurately and efficiently, and it can meet the needs of real-time detection.

Aiming at the traditional Gaussian mixture model has poor adaptability to the complex scenes, we proposes an improved moving object detection algorithm based on Gaussian mixture model and HSV space. The motion region is first extracted by the improved three-frame difference method. With the matching results, region segmentation of current frame is realized. Then different regions adopt different update strategy that improves the ability to reflect the illumination and scenes change. Next, utilizing characteristics of HSV color space and image first-order gradient achieve shadow detection. It effectively reduces interference of shadows, especially the pixels of foreground which has similar brightness properties with background. Experimental results show that the algorithm has good robustness and real-time performance.

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.

In rarefied gas flows, discontinuity phenomena such as velocity slip and temperature jump commonly appear in the gas layer adjacent to a solid boundary. Due to the physical complexity of the interactions at the gas-solid interface, particularly in the case of systems with local nonequilibrium state, boundary models with limited number of parameters cannot completely describe the reflection of gas molecules at the boundary. In this work, the Gaussian mixture (GM) model, which is an unsupervised machine learning technique, is employed to construct a statistical gas-solid surface scattering model based on the collisional data obtained from molecular dynamics (MD) simulations. The GM model is applied to study Couette flow for different inert gases (Ar and He) confined between two parallel infinite gold walls at different temperatures. A direct comparison between the results obtained from the GM model and the Cercignani-Lampis-Lord (CLL) scattering kernel against the MD collisional data in terms of the distribution of the predicted postcollisional velocities, and accommodation coefficients has shown that the results from the GM model are an excellent match with the MD results outperforming the CLL scattering kernel. As an example, for He gas, while the predicted energy accommodation coefficient by the CLL model is more than two times higher than the MD predictions, the value computed by the GM model is in excellent agreement with the MD results. This superior performance of the GM model confirms its high potential to derive a generalized boundary condition in systems encountered with highly nonequilibrium and complex gas flow conditions.

Moving object detection is critical for automated video analysis in many vision-related tasks, such as surveillance tracking, video compression coding, etc. Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporally-varying (i.e., moving) foreground objects from the static background in video, assuming the background frames to be low-rank while the foreground to be spatially sparse. Classic RPCA imposes sparsity of the foreground component using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> -norm, and minimizes the modeling error via <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _2$</tex-math></inline-formula> -norm. We show that such assumptions can be too restrictive in practice, which limits the effectiveness of the classic RPCA, especially when processing videos with dynamic background, camera jitter, camouflaged moving object, etc. In this paper, we propose a novel RPCA-based model, called Hyper RPCA, to detect moving objects on the fly. Different from classic RPCA, the proposed Hyper RPCA jointly applies the maximum correntropy criterion (MCC) for the modeling error, and Laplacian scale mixture (LSM) model for foreground objects. Extensive experiments have been conducted, and the results demonstrate that the proposed Hyper RPCA has competitive performance for foreground detection to the state-of-the-art algorithms on several well-known benchmark datasets.

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.

For the detection of moving objects, background subtraction methods are widely used. In case the background changes, we need to update the background in real-time for the reliable detection of foreground objects. An adaptive Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, the probabilistic learning approach does not work well in high traffic regions. In this paper, we classify each pixel into four different types: still background, dynamic background, moving object, and still object, and update the background model based on the classification. For the classification, we analyze a sequence of frame differences at each pixel and its neighborhood. We experimentally show that the proposed method learn complex and dynamic backgrounds in high traffic regions more reliably, compared with traditional methods.

Due to high rise in demand for video processing on hardware FPGA implementation of various image processing algorithm has become a necessity. Lot of research work is being done in the field of image processing and video processing on FPGA. In this paper we specifically concentrate on working Background identification, moving object detection and tracking. Keeping in mind the previous work dons in this area we have selected Gaussian mixture model for the implementation. The main work carried out by Gaussian mixture model is to is a common feature in many video processing systems. The paper speaks about two block of implementation mainly the image block generation and the RGB to YCbCr conversion. It has been proved from the design that it is efficient for high definition (HD) video sequences with frame size 1920 × 1080. Both the bocks are targeted on commercial field-programmable gate-array (FPGA) devices. When implemented on Atrix7 100t it uses 3% of FPGA logic resources. The second block takes like 2% of the FPGA resource. The total time required to process single frame is less than 2ns.

Moving object detection in a video plays an important role in many vision applications. Recently, moving object detection using appearance modeling based on a convolutional neural network has been actively developed. However, the CNN- based methods usually require the user’s supervision of the first frame so that it becomes highly dependent on the training dataset. In contrast, the method of finding a foreground, which models a background occupying a large proportion in an image, can detect a moving object efficiently in an unsupervised manner. However, existing methods based on background modeling in a pan-tilt-zoom (PTZ) camera suffer many false positives or loss of moving objects due to the estimation error of camera motion. To overcome the aforementioned limitations, we propose a moving object detection method for a PTZ camera through two background models. In an unsupervised way, our method builds the two background models that have different roles: 1) a coarse background model for detecting large changes, and 2) a fine background model for detecting small changes. In more detail, the coarse background model builds a block-based Gaussian model, and the fine model builds a sample consensus model. Both models are adaptively updated according to the estimated camera motion in the video recorded by a PTZ camera. Then, each foreground result from two background models is incorporated to fill the moving object region. Through experiments, the proposed method achieves better performance than the state-of-the-art methods and operates in real-time without parallel processing. In addition, we showed the effectiveness of the proposed model through improved results of moving object detection through combination with the latest supervised method.