A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> B. FEATURE BASED OBJECT TRACKING: <s> In this paper we present a robust object tracking approach which is suitable for real-time image processing on mobile devices. Challenging mobile environments render traditional color-based tracking methods useless. Many online learning tracking methods are too computationally complex to be used for real-time mobile applications, which only have access to limited computational resource and memory storage. The proposed method takes advantage of local feature to deal with rapid camera motion, and employs an online feature updating scheme to cope with variation in object appearances. The method is also computationally lightweight, being able to support real-time image processing on mobile devices. <s> BIB001 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> B. FEATURE BASED OBJECT TRACKING: <s> This paper proposes and algorithm for extended object tracking using sparse feature points. The described technique is based on the Rao-Blackwellized Particle Filter. In particular, two different data association techniques that take into consideration clutter and missed detections, are coupled and tested in order to provide a comparison of their performance for the problem of extended object tracking. <s> BIB002 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> B. FEATURE BASED OBJECT TRACKING: <s> This paper presents an effective rotating object tracking algorithm based on Multi-Part SIFT (MPS) with particle filter. SIFT describes the characteristics of feature points that are invariant to changes in illumination, image noise, rotation, scaling and view direction. In order to use proper effect of SIFT feature for target tracking, single region based object representation is not enough. Therefore, the reference and target object are divided into some sub-regions in order to extract the potential SIFT features for measurement the similarity. This solution introduces spatial information in the representation, without compromising the benefits of SIFT features. In this propose algorithm, for smooth tracking we include the rotation information into the MPS based particle filter since the object rotation directly affects the tracking performance. Additionally, for the illumination changes as well as occlusions, it is required that reference model is adaptively updated. Within the particle filtering framework, we propose to address this adaptation problem using rapidly updated the reference model. We obtained a robust result with the proposed system in particle filter framework. Experimental results show that our system is robust against false target tracking, partial occlusion problem, rotation and scaling without extra computational cost. <s> BIB003 </s> A SURVEY ON MOVING OBJECT TRACKING IN VIDEO <s> B. FEATURE BASED OBJECT TRACKING: <s> This paper presents a novel active contour tracking method, which is used to estimate the non-grid deformation of the motion target and can get the accurate contour of the tracked target. In the proposed method, level set is employed to represent the target region. By using Bhattacharyya similarity as a metric, the proposed method aims at finding a best candidate region in the video frame, whose foreground distribution and background distribution match maximally those of the predefined tracked target. Based on this metric, we derive a level set based object tracking formulation, which estimates iteratively the contour change of the target. Experimental results on the representative video sequences show that the proposed methods perform better than EM-shift and SOAMST algorithm. <s> BIB004
Li et al.  proposed a corner feature based object tracking method using Adaptive Kalman Filter. To represent moving object corner feature are firstly used. Then, the number of corner point variation across consecutive frames to is used to automatically adjust the estimate parameters of Kalman Filter. Xue et al. [15] uses the discriminative features which are chosen by object/background separation, using a voting strategy. With the help of discriminative features they presented an improved mean-shift algorithm for object tracking. Yang et al. [16] proposes a object tracking framework for forward-looking infrared (FLIR) imagery based on mean shift algorithm and feature matching. In Feature matching step they used Harris detector to extract the feature points of template object and candidate area. Moreover they have developed an improved Hausdorff distance to measure the similarity of the feature points. Aibin et al. [17] puts forward a new self-adaptive tracking algorithm views based on target center location and NMI feature. The normalized moment of inertia (NMI) features are combined to locate the center of tracking object in real-time. Mean shift algorithm is here for tracking the object. Rahman et al.  presented an improved tracking method which can track both single object and multiple objects in video sequences where the object movement may be fast or slow. The proposed method is based on background subtraction and feature matching of SIFT features. With the help of background subtraction Object is detected. Matching of motion features and SIFT features helps in detection and tracking of an object. Fazliet.al  proposed a new framework for object tracking combining sift feature and combination of color features and particle filter. SIFT features are used for target representation and localization. Local feature vector is obtained by the transformation of an image. Each of the feature vectors is invariant to image scaling, translation and rotation and illumination changes. The particle filter (PF) is used to find an approximation of the solution to the sequential estimation. Bai [21] presented a novel object tracking algorithm based on Mean Shift and on-line feature selection. In a 4-D state space, the target object is defined. Feature space is created depending on the color pixel values in R, G and B channels. The best feature space is selected during the tracking which can distinguish objects and background scenes most. In their algorithm, state estimation of the tracking objects is done with the help of Kalman filter. Miao et. al BIB001 proposed a new robust feature-based tracking method via online boosting by applying adaptive classifiers to match the detected key points in consecutive frames. The proposed approach shows that by integrating the robust local feature and the adaptive online boosting algorithm can help cater to changes between successive frames. Fan et al. [23] presented a robust object tracking for processing images on mobile devices in real-time. They employ a holistic haar-like feature matching method to track objects of interests. With the help of online feature updating scheme, robustness was achieved in their method. A feature detection method is integrated with color filtering is used to recover tracking. Kim et al. BIB002 proposed an algorithm combining background information based motion detection, feature extraction and block matching. In their method a set of features called shape control points (SCPs) are generated by detecting edges in the neighboring four directions. They have reduced the weakness of block matching algorithm with the help of an adaptive background generation method. Fan  proposed a robust tracking method. During tracking they stored the representative object appearances as candidate templates and to match new frames the best template is selected. This procedure of template adding and switching via online strategy keeps update with new object appearances. They have shown that feature-based methods can be extended to nonplanar objects or objects undergoing large pose changes. Alvarez and Regazzoni BIB003 extended their feature based method of object tracking by using sparse shape points. The possible data association events are sampled with the particle filter to Also, the particle filter helps in estimating the global position and object velocity. Biresaw et al.  developed a feature point tracker. To improve the performance of the tracker they have used time reversed back tracking evaluation criteria together with Partial Least Square regression. Hossain et al. [29] proposed a multi-Part SIFT feature based rotating object tracking object tracking observation model. The reference and target object are represented to extract the potential key points for measurement the similarity. They have used the Particle filter for solving state space estimation when the state equation is non-linear and the posterior density is non-Gaussian. Shen et al.  adopted the particle filter for tracking, which is useful for non-linear and nonGaussian problems. To find the posterior probability of particle filter they have used Bhattacharyya distance of object and the predicted position of the object obtained by the particle filter. The posterior probability is used to update the state of the filter. Their experiment proved that HSV is the optimal color space for scale variation, occlusion, and illumination change. Mahendran et al. BIB004 proposed a new tracking framework which uses Distance Metric Learning (DML) in combination with Nearest Neighbor (NN) classification for object tracking. In order to detect the object they used canny edge detector Using Nearest Neighbor classifier it is able to distinguish the object from other Objects and subtract the background from the frame using the Nearest Neighbor (NN) algorithm. The Nearest Neighbor algorithm uses the distance between the object and the background to subtract it. Then using a blob detector the object is identified on the basis of the skin color. For the identified object a bounding box is built. By applying Distance Metric Learning (DML) algorithm they tracked the object. For each frame the process was applied to track the object in real time. Liu et al. [32] proposes an improved Markov chain Monte Carlo(MCMC) named optical flow MCMC(OF-MCMC) sampling algorithm for vehicle tracking. To get the moving direction of the vehicle in initial frames they used the optical flow method, which can solve the problem of scale change and the moving object speed is obtained by autoregressive motion model. To deal with vehicle tracking in low resolution of the video data and to get better tracking results they have generated more accurate feature template with different weighted features.