Tracking method of soccer players with the likelihood of the subregion
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The moving object detection is a prerequisite and difficult point in the video tracking system to realize tracking. In order to detect moving object effectively, an object tracking algorithm is proposed based on combining dynamics template matching and Kalman filter. First, make the former two frames inter-difference to get the area of the moving object and extract the feature points. Then, find the best match with the object model candidate object location by Kalman filter in the search area and match it with the object template of the current frame. Finally, the loss rate of feature points will serve as the limited threshold, and we update template according to dynamic template update strategy. Several experiments of the object tracking show that the approach is accurate and fast, and it has a better robust performance during the posture changing, the size changing and the shelter instance.
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Visual tracking is a fundamental key to the recognition and analysis of human behavior. This paper presents an approach to track several humans from video sequences acquired in real time. It addresses the key concerns of real time performance and continuity of tracking in overlapping and non-overlapping fields of view. It represents the human body by a parametric ellipsoid in a 3D world. The elliptical boundary can be projected rapidly, several hundred times per frame, onto any image for comparison with image data within likelihood mode. This is implemented by using SIR-Particle filter for tracking multiple humans. Adding variables to encode visibility and persistence into the state vector, it tackles the problem of distraction and short period occlusion. The accuracy of the algorithm is evaluated using the metric, Multiple Object Tracking Accuracy (MOTA) and found that the accuracy was increased up to 90%, when tested with the benchmark dataset.
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A robust algorithm solution is proposed for tracking an object in complex video scenes. In this solution, the bootstrap particle filter (PF) is initialized by an object detector, which models the time-evolving background of the video signal by an adaptive Gaussian mixture. The motion of the object is expressed by a Markov model, which defines the state transition prior. The color and texture features are used to represent the object, and a marginal likelihood based feature fusion approach is proposed. A corresponding object template model updating procedure is developed to account for possible scale changes of the object in the tracking process. Experimental results show that our algorithm beats several existing alternatives in tackling challenging scenarios in video tracking tasks.
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【Abstract】An improved player tracking algorithm based on multi-feature adaptive fusion is proposed to solve existing problems that the model-based tracking method is difficult to deal with greater change of players’ form in football video. This paper uses the adaptive Gaussian mixture model to detect football playfield and players. The Bhattacharyya distance of players’ HUE color features is used to distinguish ownership of the team instead of traditional template matching methods. The method fuses the color, shape and temporal-spatial feature information of target model adaptively for tracking the players and uses three-point prediction method to solve the complete occlusion between players. Experimental results show that the algorithm deals well with the occlusion between players, and can track robustly when the players’ shape changes greatly. 【Key words】adaptive weight; feature fusion; temporal-spatial feature; three-points estimation; complete occlusion; object tracking DOI: 10.3969/j.issn.1000-3428.2012.17.058 计 算 机 工 程 Computer Engineering 第 38卷 第 17期 Vol.38 No.17 2012年 9月 September 2012
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In this paper, we propose a patch-based object tracking algorithm which provides both good enough robustness and computational efficiency. Our algorithm learns and maintains Composite Patch-based Templates (CPT) of the tracking target. Each composite template employs HOG, CS-LBP, and color histogram to represent the local statistics of edges, texture and flatness. The CPT model is initially established by maximizing the discriminability of the composite templates given the first frame, and automatically updated on-line by adding new effective composite patches and deleting old invalid ones. The inference of the target location is achieved by matching each composite template across frames. By this means the proposed algorithm can effectively track targets with partial occlusions or significant appearance variations. Experimental results demonstrate that the proposed algorithm outperforms both MIL and Ensemble Tracking algorithms.
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The moving object detection is a prerequisite and difficult point to realize tracking in the video tracking system. In order to detect moving object effectively, an object tracking algorithm is proposed based on combination of dynamic template matching and Kalman filter. First, get the area of the moving object by using inter-frame difference method and extract the SIFT feature points. Then, find the location of the candidate object that is most matched with the object template in the search area by Kalman filter and match it with the object template in the current frame. Finally, the feature points' loss rate will serve as the limited threshold, and we update template according to dynamic template updating strategy. By the number of the frames of the target's matching failures we determine whether the moving target is disappeared. Several experiments of the object tracking show that the approach is accurate and fast, and it has a better robust performance during the attitude changing, the size changing and the shelter instance.
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The research of object tracking method based on sequence image has become an important content of computer vision field.An accuracy,real-time and robust tracking system is important in research.In this paper we offer an improved method of object tracking based on particle filter algorithm.We extract the object HSI color histogram as object template and construct system state transition model and observation model.We also use resample technology and finally estimate the position and shape of object using particles weighted sum.Through the software simulation,the algorithm described in this paper has better characteristics of real-time,accuracy and robust than conventional object tracking algorithm.
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