Research on A Target Tracking System Based on DM-6437-355
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In this paper,a double tracking algorithm target tracking system based on DM6437-355 development board combining with OpenCV and VC technology is designed. Both the centroid tracking algorithm and the center of gravity tracking algorithm are set into the system,the centroid tracking algorithm is an improvement based on threshold image segmentation pretreatment,the center of gravity tracking algorithm is proposed for setting different weights I(x,y) for each sample point,it is an improvement solving the coordinates of the center of gravity. The phenomenon has been significantly improved,that the tracked target is lost by the target rolling and deforming and partially being shaded. A different tracking algorithm depending on different situations is chosen,the target tracking results in the midst of a real environment is strengthened.Keywords:
Centroid
Tracking (education)
Center of gravity
Tracking system
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The target tracking methods based respectively on feature,correlation and contrast are introduced with advantages and disadvantages of each algorithm.Then,an improved correlation tracking algorithm is put forward according to the adaptability requirement to the algorithm under various complex environment.In the algorithm,pyramid searching algorithm is used for image preprocessing to decrease the resolution of the matched image.Then correlation matching algorithm is carried out.Experiment results indicate that this method can improve algorithm's adaptability to automatic target tracking.
Adaptability
Pyramid (geometry)
Tracking (education)
Feature (linguistics)
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An improved correlation matching algorithm is proposed in order to overcome some shortcomings of detecting the position of object accurately. A tracking algorithm with normalized cross correlation is introduced. In order to enhance the match speed, we have adopted pyramid search algorithm. The experimental results show that the algorithm has characteristics including automatic recognition of the object; permitting tracking and prediction when the object become shaded; the algorithm makes adaptive decision of varied object during the process of tracking.
Tracking (education)
Pyramid (geometry)
Position (finance)
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We propose a novel tracking algorithm which can work robustly under complex dynamic scenarios. Our algorithm is based on a scheme of multiple basic mean shift tracking. In this scheme, we use Sparse Principal Component Analysis to generate multiple target models, with which each basic mean shift tracker runs in parallel at the same time. The best configuration of a target is obtained by the weighted linear combination of its basic results. In addition, for the problem that the histogram of gradient under the mean shift tracking framework is easy to fall into local maxima, we introduce the histogram of Gradient Vector Flow to represent the target. Experimental results show that our tracker is able to handle severe appearance change and recover from drifts in realistic videos. The algorithm proposed in this paper can track the target accurately and reliably compared with other existing state-of-the-art tracking algorithms.
Mean-shift
Tracking (education)
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As the traditional Mean-Shift tracking algorithm fix the size and orientation of tracking window, it can not effectively track the target when the scale and orientation of tracking target has a distinct change. In response to this problem, the proposed algorithm which combines target contour and the principal components of the variance matrix with Mean-Shift tracking algorithm. It uses the target contour Information of the real-time to get the current size of the target and uses the principal components to compute the orientation of the tracking target. Experimental results show that the improved algorithm has a good adaptability when the scale and orientation of the target change.
Mean-shift
Tracking (education)
Adaptability
Tracking system
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AbstractThis paper describes the design of a real-time raster-scan TV/FLIR camera based tracking system for fast moving objects using image processing techniques. A tracking-window is placed over the approximate location of the target, either automatically or by a human operator. The target image is segmented from the background using a new adaptive cooperative segmentation technique that utilizes background histogram in the immediate vicinity of the target-image and edge strengths of the pixels within the tracking window. The segmented target-image is then processed to estimate tracking-errors and compute a confidence measure. Tracking errors are filtered using a stable implementation of the Kalman filter to get accurate estimates of the target's motion-states. The target state is predicted over the next few image frames for generating orientation commands for the tracking-mount. The tracking-system successfully tracks targets even under low-contrast and noisy imaging conditions.Indexing terms: Real-time trackingTarget-trackingSegmentationKalman filtering
Tracking (education)
Tracking system
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The idea of the adaptive image matching tracking algorithm,then with position estimation and image template renewal and could effectively cope with complicated nonlinear and/or non-Gaussian problems.In order to reduce the amount of calculation of tracking algorithm based on Hausdorff distance,updated with the method of combining is adopted.The adaptive image matching target tracking algorithm based on the matching tracking is presented and the performance of the algorithm is analyzed.The results show that adaptive particle tracking algorithm inherited the relevant practical tracking visual characteristics,the part occlusion is effectively solved and higher stability of the match tracking.
Tracking (education)
Position (finance)
Template matching
Hausdorff distance
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Owing to fuzzy detail and distortion of underwater image and complex changes of target, the underwater target tracking system requires accuracy and continuity of tracking, and expects that the size of tracking window can adapt to appearance change of target. According to the requirements mentioned above, the underwater target tracking algorithm based on an improved color matching is proposed, which finds the best location of target through tracking accuracy algorithm and calculates width of window on the basis of tracking window size variation algorithm. The experimental results show that this algorithm can adaptively track the real-time target and has higher accuracy than traditional color matching algorithm.
Tracking (education)
Distortion (music)
Tracking system
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A new dual-mode tracking algorithm which was the combination of the correlation tracking algorithm and centroid tracking algorithm is developed.According to this algorithm,a real-time tracking software systems have been built.It could be used to track the ground stationary objects.The strategy of target recognition is analyzed.Whether the target is lost on tracking and how the target could be tracked again in case of it is lost is also discussed.
Tracking (education)
Centroid
Tracking system
Mode (computer interface)
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The paper presents a new efficient method for performance evaluation of imaging seeker tracking algorithm. The method utilizes multi features which associate with tracking point of each video frame, gets local score(LS) for every feature, and achieves global score(GS) for given tracking algorithm according to the combined strategy. The method can be divided into three steps. In a first step, it extracts evaluation feature from neighbor zone of each tracking point. The feature may include tracking error, shape of target, area of target, tracking path, and so on. Then, as to each feature, a local score can be got rely on the number of target which tracked successfully. It uses similarity measurement and experiential threshold between neighbor zone of tracking point and target template to define tracking successful or not. Of course, the number should be 0 or 1 for single target tracking. Finally, it assigns weight for each feature according to the validity grade for the performance. The weights multiply by local scores and normalized between 0 and 1, this gets global score of certain tracking algorithm. By compare the global score of each tracking algorithm as to certain type of scene, it can evaluate the performance of tracking algorithm quantificational. The proposed method nearly covers all tracking error factors which can be introduced into the process of target tracking, so the evaluation result has a higher reliability. Experimental results, obtained with flying video of infrared imaging seeker, and also included several target tracking algorithms, illustrate the performance of target tracking, demonstrate the effectiveness and robustness of the proposed method.
Tracking (education)
Feature (linguistics)
Tracking system
Similarity (geometry)
Tracking error
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Aiming at the tracking problem of targets in video sequence images,a multi-target tracking algorithm based on region matching is proposed.This algorithm can extract the grey level distribution features in a target region.In the matching process of targets,it uses the Bhattacharyya modulus to search the most similar regions in images.The simulation result shows that the tracking performance of this algorithm is much better than that of other feature matching and multi-model algorithms.The algorithm has a small computation load and can ensure the real-time performance in target tracking.
Bhattacharyya distance
Tracking (education)
Mean-shift
Feature (linguistics)
Sequence (biology)
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