A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> B. Region-based object tracking model <s> The problem considered in this paper is how to select the feature points (in practice, small image patches are used) in an image from an image sequence, such that they can be tracked adequately further through the sequence. Usually, the tracking is performed by some sort of local search method looking for a similar patch in the next image in the sequence. Therefore, it would be useful if we could estimate "the size of the convergence region" for each image patch. There is a smaller chance of error when calculating the displacement for an image patch with a large convergence region than for an image patch with a small convergence region. Consequently, the size of the convergence region can be used as a proper goodness measure for a feature point. For the standard Kanade-Lucas-Tomasi (KLT) tracking method, we propose a simple and fast way to approximate the convergence region for an image patch. In the experimental part, we test our hypothesis on a large set of real data. <s> BIB001 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> B. Region-based object tracking model <s> Video-based surveillance systems have a wide range of applications for traffic monitoring, as they provide more information as compared to other sensors. In this paper, we present a rule-based framework for behavior and activity detection in traffic videos obtained from stationary video cameras. Moving targets are segmented from the images and tracked in real time. These are classified into different categories using a novel Bayesian network approach, which makes use of image features and image-sequence-based tracking results for robust classification. Tracking and classification results are used in a programmed context to analyze behavior. For behavior recognition, two types of interactions have mainly been considered. One is interaction between two or more mobile targets in the field of view (FoV) of the camera. The other is interaction between targets and stationary objects in the environment. The framework is based on two types of a priori information: 1) the contextual information of the camera's FoV, in terms of the different stationary objects in the scene and 2) sets of predefined behavior scenarios, which need to be analyzed in different contexts. The system can recognize behavior from videos and give a lexical output of the detected behavior. It also is capable of handling uncertainties that arise due to errors in visual signal processing. We demonstrate successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions. <s> BIB002
The region based object model bases it's tracking of objects on the color distribution of the tracked object BIB002  . It represents the object based on the color. Hence, it is computationally efficient. However, its efficiency is degraded when several objects move together in the image sequences. It is not possible to achieve accurate tracking when multiple objects move due to occlusion. Also, in the absence of any object shape information, the object tracking is largely dependent on the background model used in the extraction of the object outlines. C. Feature point based tracking algorithm In Feature point based model feature points is used to describe the objects BIB001  . There are three basic steps in feature point based tracking algorithm. The first step is to recognize and track the object by extracting elements. The second step is to cluster them into higher level features. The last step is to match these extracted features between images in successive frames. Feature extraction and feature correspondence are the important steps of feature based object tracking. The challenging problem in feature point based tracking is feature correspondence because a feature point in one image may have many similar points in another image, and hence results in feature correspondence ambiguity.