A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> C. REGION BASED OBJECT TRACKING: <s> This paper introduces a novel semantic video object tracking system using backward region-based classification. It consists of five elementary steps: region pre-processing, region extraction, region-based motion estimation, region classification and region post-processing. Promising experimental results have shown solid performance of this generic tracking system with pixel-wise accuracy. <s> BIB001 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> C. REGION BASED OBJECT TRACKING: <s> With the popularity of MPEG-4 and MPEG-7 standards, video object (VO) segmentation becomes a very challenging research area in video applications. We present a novel method for semi-automatic object segmentation for a video sequence. The proposed approach starts with a rough user input VO definition. It then combines each frame's region segmentation and motion estimation results to construct the objects of interest for temporal tracking this object along the time. An active contour model based algorithm is employed to further fine-tune the object's contour so as to extract accurate object boundary. Some experimental results and future research directions are also discussed. <s> BIB002 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> C. REGION BASED OBJECT TRACKING: <s> This paper introduces techniques for segmentation and tracking based on analysis of region derived descriptors. By partitioning the image into a series of homogeneous regions, the problem of object extraction changes from pixel based to database analysis. A region based approach has distinct advantages over pixel. In particular, it has low dimensionality, is resilient to rotation, shear, and photometric changes, preserves boundaries, gives implied segmentation, provides comprehensive description, has reduced drift, and permits detailed analysis. The method is amenable to the introduction of prior knowledge permitting the simplification of segmentation, which is otherwise an ill-posed problem. Objects are defined as collections of contiguous regions with known statistics, allowing their identification by performing correlations in the database. The interframe difference of region based sequences is high for low-motion scenes due to segmentation noise, but performs consistently better for high motion, making it suitable for tracking. When applied to tracking the method proves robust, occlusion insensitive, and unlike the other techniques, it re-establishes lost lock. <s> BIB003 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> C. REGION BASED OBJECT TRACKING: <s> Usually, the video based object tracking deal with non-stationary image stream that changes over time. Robust and Real time moving object tracking is a problematic issue in computer vision research area. Multiple object tracking has many practical applications in scene analysis for automated surveillance. If we can track a particularly selected object in an environment of multiple moving objects, then there will be a variety of applications. In this paper, we introduce a particular object tracking in an environment of multiple moving objects. When tracking, we need to analyze video sequences to track object in each frame. In this paper, we use a differential image of region-based tracking method for the detection of multiple moving objects. In other to ensure accurate object detection in unconstrained environment, we also use a method of background image update. There are problems in tracking a particular object through a sequence of video. It can't rely only on image processing techniques. Thus we solved these problems using a probabilistic framework. Particle filter has been proven to be a robust algorithm to deal with the nonlinear, non-Gaussian problems. In this paper, the particle filter provides a robust object tracking framework under ambiguity conditions and greatly improved estimation accuracy for complicated tracking problems. <s> BIB004 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> C. REGION BASED OBJECT TRACKING: <s> In this paper, we propose an efficient unsupervised method for moving object detection and tracking. To achieve this goal, we use basically a region-based level-set approach and some conventional methods. Modeling of the background is the first step that initializes the following steps such as objects segmentation and tracking. Our proposed method produces good results and decreases processing time. We present here the main steps of our method and preliminary results which are very encouraging for many applications such as video surveillance and traffic monitoring. <s> BIB005 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> C. REGION BASED OBJECT TRACKING: <s> In this work we present a region-based particle filter for generic object tracking and segmentation. The representation of the object in terms of regions homogeneous in color allows the proposed algorithm to robustly track the object and accurately segment its shape along the sequence. Moreover, this segmentation provides a mechanism to update the target model and allows the tracker to deal with color and shape variations of the object. The performance of the algorithm has been tested using the LabelMe Video public database. The experiments show satisfactory results in both tracking and segmentation of the object without an important increase of the computational time due to an efficient computation of the image partition. <s> BIB006 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> C. REGION BASED OBJECT TRACKING: <s> This paper proposes a novel tool detection and tracking approach using uncalibrated monocular surgical videos for computer-aided surgical interventions. We hypothesize surgical tool end-effector to be the most distinguishable part of a tool and employ state-of-the-art object detection methods to learn the shape and localize the tool in images. For tracking, we propose a Product of Tracking Experts (PoTE) based generalized object tracking framework by probabilistically-merging tracking outputs (probabilistic/non-probabilistic) from time-varying numbers of trackers. In the current implementation of PoTE, we use three tracking experts - point-feature-based, region-based and object detection-based. A novel point feature-based tracker is also proposed in the form of a voting based bounding box geometry estimation technique building upon point-feature correspondences. Our tracker is causal which makes it suitable for real-time applications. This framework has been tested on real surgical videos and is shown to significantly improve upon the baseline results. <s> BIB007
Xu et al. BIB002 presented a new method for supervised object segmentation in video sequence. In the proposed method the user input object outline is considered as video object. In moving object tracking, the model incorporated the object's region segmentation and the motion estimation. Active contour model is also employed for contour fine-tuning. Gu and Lee BIB001 introduced video object tracking system using backward region based classification. Their system consists of five steps, pre-processing of region, region extraction, motion estimation based on region, region classification and post-processing of the region. Semantic video object boundary is found using a combination of morphological segmentation tool and human assistance. Motion estimation, semantic video object compensation and Iframes boundary information is taken to find out other video objects in the remaining frames. The object tracking algorithm proposed by Hariharakrishnan and Schonfeld BIB003 avoids segmentation only for the reason that object partition is initialized in the initial frame. Tracking is done by object boundary prediction using block motion vectors and then updating of object contour by occlusions/disocclusion detection method. For estimating motion between frames they used an adaptive block-based approach. The modification of disocclusion detection algorithm helps in developing occlusion detection algorithm by considering duality principle. Andrade et al. [37] introduced a novel technique with the help of region derived descriptors for segmentation and tracking. The homogeneous regions of an image are obtained by partitioning the image into a series. Thus, the problem of object extraction changes from pixel based to database analysis. Wei et al. BIB004 proposed an object extraction scheme mainly consists of two trackers. Using Adaboost-based global color feature selection the pixel-wise tracker extracts an object. To regionalize each frame K-means clustering is performed by the region-wise tracker at the beginning. Using a bidirectional labeling scheme region tracking is achieved. Kim and Sim BIB005 proposed a region-based tracking method for the detection of multiple moving objects which uses a differential image. A method of background image update is applied to ensure accurate object detection in unconstrained environment. They have applied the particle filter which provides a robust object tracking framework under complex conditions and greatly improved estimation accuracy for complicated tracking problems. Khraief et al. BIB006 presented algorithm for detecting and tracking moving objects using automatic initialization based on background modeling. Their proposed region competition level-set method was used for motion detection and tracking based on the statistical information of image intensity within each subset instead of searching geometrical boundaries. Before going to object segmentation and tracking background modeling is done. Varas and Marques BIB007 presented a region-based particle filter for generic object tracking and segmentation. Their algorithm combines color based particle filter and region based particle filter. The algorithm tracked objects in a reliable manner and also provides an accurate segmentation of the target during the sequence. The particle filters uses multiple hypotheses for tracking objects. Wu et al.  developed a robust 3D tracking model which is capable of extract object independent motion trajectory under uncontrolled environment. They have designed two novel algorithms, including a motion-based segmentation and a region-based Mean-shift tracking approach. A Kalman filter is applied to fuse their tracking results of the two algorithms.