A Survey on Video Transmission Techniques <s> Target Tracking <s> It is possible to index and manage large video archives in a more efficient manner by detecting and recognizing text within video frames. There are some inherent properties of videotext, such as distinguishing texture, higher contrast against background, and uniform color, making it detectable. By employing these properties, it is possible to detect text regions and binarize the image for character recognition, in this paper, a complete framework for detection and recognition of videotext is presented. The results from Gabor-based texture analysis, contrast-based segmentation and color homogeneity are merged to obtain minimum number of candidate regions before binarization. The performance of the system is tested for its recognition rate for various combinations and it is observed that the results give recognition rates, reasonable for most practical purposes. <s> BIB001 </s> A Survey on Video Transmission Techniques <s> Target Tracking <s> The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects. <s> BIB002 </s> A Survey on Video Transmission Techniques <s> Target Tracking <s> Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients. We first present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using Gaussian mixture models (GMMs). We show that GMM-based modeling alone cannot capture the temporal relations and ordering between underlying entities. To address this issue, we use hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology (e.g., left-right versus ergodic). Experiments using a database of over 5700 complex trajectories (obtained from UCI-KDD data archives and Columbia University Multimedia Group) subdivided into 85 different classes demonstrate the superiority of our proposed HMM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature. <s> BIB003
Target tracking technique BIB001 is a process where one or several moving targets are located in videos within a period of time. Target tracking need support of target recognition program, and is a time-consuming process. Target tracking is an important direction in the field of computer technology and has been widely applied to many fields like science and technology, national defense construction, aviation and space navigation, medicine and health and national economy. Selecting the right features plays a critical role in tracking BIB002 . Many tracking algorithms use a combination of these features. The details of common visual features are as follows. Color: The apparent color of an object is influenced primarily by two physical factors of the spectral power distribution of the illuminant and the surface reflectance properties of the object. Edge:. Object boundaries usually generate strong changes in image intensities. Edge detection is used to identify these changes. Optical Flow: Optical flow is a dense field of displacement vectors which defines the translation of each pixel in a region. Texture: Texture is a measure of the intensity variation of a surface which quantifies properties such as smoothness and regularity. By partition targets reasonably, extract features accurately and perform matching of feature information, we can recognize targets correctly. At the same time, run time of algorithm must be considered in order to ensure real-time performance. When moving speed of a target exceeds frame rate or the target constantly changes its direction, target tracking will have some difficulty. The main challenge that target tracking faces with is that uncertainty of environment results in the situation that it is difficult to estimate status of targets. Additionally, factors like changes in illumination and shapes of targets, complicated background and covering affects robustness of tracking. In order to overcome such difficulty, researchers put forward many target tracking methods. Common target tracking algorithms BIB002 include mean shift, Kalman filtering and particle filter etc. Mean shift is a nonparametric clustering technique, which neither needs priori knowledge about quantity of clusters nor restricts shapes of clusters and it acts as an iterative algorithm, as well. Firstly, it computes mean shifting value of the current point and moves the point to shifting mean vector. Then, it uses the points as a new starting point, continues the foregoing process and ends the iterative process until some conditions are satisfied. It was in 1975 that the method was proposed for the first time. Since then, it has been widely studied in the field of video analysis and huge progress has been achieved. Mean shift algorithm is a nonparametric fast mode matching algorithm based on kernel density estimation. With respect of variable a the finite data set in Euclidean n-spaces, sample average near the point x (x X) is defined as: In the foregoing formula, K is a kernel function, stands for weight function and the difference value sm(x)-x is called mean shift vector. Mean shift algorithm moves data points towards direction of the mean shift vector repeatedly and constantly until convergence is eventually obtained. Movement step is a fixed value. At this time, position of kernel center is corresponding to extreme value of a certain probability density, i.e., position of schema. Basic thought of Kalman filter is that it does not consider impacts of input signal and observation noise first but obtains estimated values of state variable and output signal and corrects the estimated value of the state variable after the evaluated error of the output signal has been used for weighting to make mean square error of evaluated error of the state variable be minimum. Particle filter is a filtering algorithm based on Bayesian reasoning. It is realized by non-parametric sequential Monte Carlo simulation method. Besides, particle filter is not restricted by the situation that system should satisfy linearity and Gaussian distribution. Thus, it has been widely applied to many aspects like navigation, machine vision and target tracking. In the particle filter, many features can be used. Color and edge contour BIB003 are two common features that express targets. The main difference between particle filter algorithm and the mean shift algorithm is that the way in which they look for extreme value of similarity & probability density distribution is different. In detail, the mean shift look for position of extreme value by iteration, while the particle filter does not adopt the iteration process but spreads a number of particles by which it obtains position of extreme value. In addition, since particle sampling is spread to the whole similarity & probability density space, it has global optimum nature. The mean shift that is used for tracking belongs to deterministic algorithm, and the particle filter belongs to a statistical method. Compared with the particle filter, the mean shift tracking algorithm has better real-time performance. However, the particle filter holds better tracing accuracy theoretically.