A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> LITERATURE SURVEY A. CONTOUR BASED OBJECT TRACKING: <s> In this paper, we present an object contour tracking approach using graph cuts based active contours (GCBAC). Our proposed algorithm does not need any a priori global shape model, which makes it useful for tracking objects with deformable shapes and appearances. GCBAC are not sensitive to initial conditions and always converge to the optimal contour within the dilated neighborhood of itself. Given an initial boundary near the object in the first frame, GCBAC can iteratively converge to an optimal object boundary. In each frame thereafter, the resulting contour in the previous frame is taken as initialization and the algorithm consists of two steps. In the first step, GCBAC are applied to the difference between this frame and its previous one. The resulting contour is taken as initialization of the second step, which applies GCBAC to current frame directly. To evaluate the tracking performance, we apply the algorithm to several real world video sequences. Experimental results are provided. <s> BIB001 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> LITERATURE SURVEY A. CONTOUR BASED OBJECT TRACKING: <s> Object tracking is a problem which is very often addressed by various approaches, including background subtraction, visual constancy or geometric flow. The contribution of this paper is twofold. First, it proposes an enhanced feature-weighted gradient, enhancing the contours of the object characterized by this feature, and attenuating the gradient amplitude wherever the feature is not found. The second, and main contribution, is a new integrator for active-contours based tracking. It uses the gradient alongside the object contours and generates a bidirectional attraction field. The combination of the contour tracking and the weighted gradient is illustrated on the driver's-face tracking problem, where the illumination conditions (brightness, colour) change as the vehicle passes through different environments. <s> BIB002 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> LITERATURE SURVEY A. CONTOUR BASED OBJECT TRACKING: <s> The computer image object tracking technologies are often applied to various kinds of research fields. It proposes real-time tracking object recognition by contour-based neural fuzzy network. It employs the active contour models and neural fuzzy network method to trace moving objects of the same kind and to record its paths simultaneously. To extract object’s feature vector, it uses contour-based model. The traditional background subtraction and object segmentation algorithms are modified to reduce operation complexity and achieve real-time performance. Finally, it uses the self-constructing neural fuzzy inference network to train and recognize moving objects. The experiment shows it can recognize four moving objects, including a pedestrian etc., exactly. The experiment result shows the precision of this system is more than 90% under objects tracking, and the frame rate is more than 25 frames per second. <s> BIB003 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> LITERATURE SURVEY A. CONTOUR BASED OBJECT TRACKING: <s> This letter presents a two-stage object tracking method by combining a region-based method and a contour-based method. First, a kernel-based method is adopted to locate the object region. Then the diffusion snake is used to evolve the object contour in order to improve the tracking precision. In the first object localization stage, the initial target position is predicted and evaluated by the Kalman filter and the Bhattacharyya coefficient, respectively. In the contour evolution stage, the active contour is evolved on the basis of an object feature image generated with the color information in the initial object region. In the process of the evolution, similarities of the target region are compared to ensure that the object contour evolves in the right way. The comparison between our method and the kernel-based method demonstrates that our method can effectively cope with the severe deformation of object contour, so the tracking precision of our method is higher. <s> BIB004 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> LITERATURE SURVEY A. CONTOUR BASED OBJECT TRACKING: <s> particle filter is a probabilistic multi-hypothesis algorithm under the Bayesian framework. In order to establish a robust observing model, in this paper, a novel method which uses a more effective color feature with contour information integrated adaptively is proposed. Experimental results verified that, our approach was efficient. <s> BIB005 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> LITERATURE SURVEY A. CONTOUR BASED OBJECT TRACKING: <s> In this paper, a novel object contour tracking framework integrating independent multi-feature fusion object rough location and region-based temporal differencing model is proposed. In our model, the object rough location tracking is realized by color histogram and Harris corner features fusion method in particle filter framework. Thus it can achieve more robust tracking performance in many challenge scenes. And this particle filter framework is based on our previous CamShift guided particle filter [7]. With the rough object location, efficient region-based temporal differencing model is adopted for object contour detection, then this method is faster and more effective compared to active contour models or conventional global temporal differencing models. Moreover, exact contour tracking result can be used to guide the particle propagation of next frame, to enable more efficient particle redistributions and reducing particle degeneration. Experimental results demonstrate that this proposed method is simple but effective in object location and contour tracking. <s> BIB006 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> LITERATURE SURVEY A. CONTOUR BASED OBJECT TRACKING: <s> In this paper, we present a framework for active contour-based visual tracking using level sets. The main components of our framework include contour-based tracking initialization, color-based contour evolution, adaptive shape-based contour evolution for non-periodic motions, dynamic shape-based contour evolution for periodic motions, and the handling of abrupt motions. For the initialization of contour-based tracking, we develop an optical flow-based algorithm for automatically initializing contours at the first frame. For the color-based contour evolution, Markov random field theory is used to measure correlations between values of neighboring pixels for posterior probability estimation. For adaptive shape-based contour evolution, the global shape information and the local color information are combined to hierarchically evolve the contour, and a flexible shape updating model is constructed. For the dynamic shape-based contour evolution, a shape mode transition matrix is learnt to characterize the temporal correlations of object shapes. For the handling of abrupt motions, particle swarm optimization is adopted to capture the global motion which is applied to the contour in the current frame to produce an initial contour in the next frame. <s> BIB007 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> LITERATURE SURVEY A. CONTOUR BASED OBJECT TRACKING: <s> Object tracking is an important task in video applications. In this paper, we present a modified contour-based algorithm for multiple objects tracking and detection. The advantages of this algorithm are accurate detecting and tracking objects in overlapping areas and also surveying the position of objects in the next moment improving the speed of tracking. Thus, this algorithm is capable to be exploited in real-time applications. In the presented algorithm the speed and location of selected number of contour points are used, instead of the contour itself. The experimental trials demonstrate promising results. <s> BIB008
Xu and Ahuja BIB001 proposed a contour based object tracking algorithm to track object contours in video sequences. In their algorithm, they segmented the active contour using the graph-cut image segmentation method. The resulting contour of the previous frame is taken as initialization in each frame. New object contour is found out with the help of intensity information of current frame and difference of current frame and the previous frame. Dokladal et al. BIB002 the proposed approach is active contour based object tracking. For the driver's-face tracking problem they used the combination of feature-weighted gradient and contours of the object. In the segmentation step they computed the gradient of an image. They proposed a gradient-based attraction field for object tracking Chen BIB003 models an active contour based object tracking by Neural Fuzzy network.Contourbased model is used to extract object's feature vector. For training and recognizing moving objects their approach uses the self-constructing neuralfuzzy inference network. In this paper, they have taken the histograms of the silhouette of human body in horizontal and vertical projection and then transform it by Discrete Fourier Transform (DFT). Chen BIB004 proposed object tracking method consisting of two-stages. Firstly the kernel-based method is used to locate the object in complex environment like partial occlusions, clutter, etc. To improve the tracking result they again used contour based method and tracked the object contour precisely after the target localization. In the target localization step with the of Kalman filter and the Bhattacharyya coefficientthe initial targetposition is predicted and evaluated Zhou et al. BIB005 uses the integration of color feature and contour information in the particle filter based multi-hypothesis tracking algorithm. For the contour detection they have used sobel operator and the shape similarity is evaluated between the observing position and the sample position by corresponding points matching in the two contour images. Ling et al. BIB006 given an object tracking approach based on contours. The object rough location is found though multi-feature fusion strategy. For accurate and robust object contour tracking, they have extracted the contours with the help of region-based object contour extraction. In their model the object rough location is obtained by color histogram and Harris corner features fusion method. In the particle filer method they have used the Harris corner feature fusion method. Their model of region-based temporal differencing is applied in object contour detection step, and the resultant is the rough location tracking result. Hu et al. BIB007 proposed an effective framework for tracking object contours. Their proposed framework integrated different model such as tracking initialization algorithm, color -based contour evolution algorithm, and adaptive shape-based contour evolution and Markov modelbased dynamical shape model. Optical flow detection is used in automatic and fast tracking initialization algorithm. In colorbased contour evolution algorithm the correlations between values of neighboring pixels for posterior probability estimation is measured using Markov random field (MRF) theory and the correlations are incorporated into the estimation of the posterior probability of segmentation. Their adaptive shape-based contour evolution algorithm combines the color feature alone and the shape priors to obtain the final contour. A new incremental PCA technique is applied to update the shape model, making the shape model updating flexible. In the Markov model-based dynamical shape model, the dominant set clustering is used to obtain the typical shape modes of a periodic motion. Rajabi and Nahvi BIB008 proposed a modified contour-based multiple object tracking algorithm using point processing. This approach has the advantage of multiple objects tracking. Their system can detect and track the peoples in indoor environments videos. In their method they have used Gaussian mixture model (GMM) based background modeling for background estimation.