A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> Background modeling Based Object Detection <s> A new probabilistic background model based on a Hidden Markov Model is presented. The hidden states of the model enable discrimination between foreground, background and shadow. This model functions as a low level process for a car tracker. A particle filter is employed as a stochastic filter for the car tracker. The use of a particle filter allows the incorporation of the information from the low level process via importance sampling. A novel observation density for the particle filter which models the statistical dependence of neighboring pixels based on a Markov random field is presented. The effectiveness of both the low level process and the observation likelihood are demonstrated. <s> BIB001 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> Background modeling Based Object Detection <s> Hidden Markov models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Their use involves an off-line learning step that is used as a basis for on-line decision making (i.e. a stationarity assumption on the model parameters). But, real-world applications are often non-stationary in nature. This leads to the need for a dynamic mechanism to learn and update the model topology as well as its parameters. This paper presents a new framework for HMM topology and parameter estimation in an online, dynamic fashion. The topology and parameter estimation is posed as a model selection problem with an MDL prior. Online modifications to the topology are made possible by incorporating a state splitting criterion. To demonstrate the potential of the algorithm, the background modeling problem is considered. Theoretical validation and real experiments are presented. <s> BIB002 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> Background modeling Based Object Detection <s> Although trivial background subtraction (BGS) algorithms (e.g. frame differencing, running average...) can perform quite fast, they are not robust enough to be used in various computer vision problems. Some complex algorithms usually give better results, but are too slow to be applied to real-time systems. We propose an improved version of the Extended Gaussian mixture model that utilizes the computational power of Graphics Processing Units (GPUs) to achieve real-time performance. Experiments show that our implementation running on a low-end GeForce 9600GT GPU provides at least 10x speedup. The frame rate is greater than 50 frames per second (fps) for most of the tests, even on HD video formats. <s> BIB003
Gaussian Mixture Model: Knowing the moving object distribution in the first frame o f the video sequences, we can localize the object in the next frames by tracking its distribution. Gaussian Mixture Model is a popular technique for modeling dynamic background as it can represent complex distribution of each pixel. But GMM suffers from slow convergence at the starting stage of detecting backgrounds. Also it sometimes leads to false motion detection in complex background. Figure2: Segmentation using GMM based background modeling. Foreground pixels are marked in white BIB003 Stauffer and Grimson  use a mixture of Gaussians to model the pixel color. In this method, every pixel value of current frame is checked against the existing Gaussian distributions of the background model. Until a matching Gaussian is found the pixel value are checked continuously in the model. The mean and variance of the matched Gaussian is updated when a match is found. If this pixel value does not fit into any one of the Gaussian distributions, the distribution with the least weight is replaced by a new distribution mean as current pixel value, with high variance at initial stage, and a low weight. Classification of pixels is done based on whether matched distribution represents the background process. B. Eigen-space Decomposition of Background: Another approach for background modeling based object detection is Eigen-space decomposition. It is less sensitive to illumination. Here, by projecting the current image to the eigen-space and calculating the difference between the reconstructed and actual images, the foreground objects are detected. Suppose there are k input frames, I i : i= 1 · · · k , of size n×m. Now by cascading m rows in each frame one after another a background matrix B of size k ×l is formed, where l = (n×m). The eigenvalue decomposition is applied to the covariance of B, i.e. C = B T B. The most descriptive η eigenvectors u i , where i<η< k, represents the background, which encompass all possible illuminations in the field of view (FOV). C. Hidden Markov Model: I n r e c e n t d a y s Hidden Markov Model is widely used for background subtraction. Corresponding to the events in the environment i t represents the intensity variations of a pixel in an image sequence as discrete states. As for example, image pixels can be in the background state, the foreground (car) state, or the shadow state in highway car tracking scenario. Hidden Markov Models (HMM) u s e d b y Rittscher et al. BIB001 classified small blocks of an image into the above three states. Stenger et al. BIB002 use HMMs for the background subtraction in the context of detecting light on /off events in a room. Those events which are hard to model correctly using unsupervised background modeling approach advantage HMMs are used for training samples.