In order to improve the accuracy of polymorphic pedestrian target recognition and tracking, and shorten tracking time, this paper proposes a public place polymorphic pedestrian target recognition and tracking algorith...
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In order to improve the accuracy of polymorphic pedestrian target recognition and tracking, and shorten tracking time, this paper proposes a public place polymorphic pedestrian target recognition and tracking algorithm based on the camshift algorithm. Firstly, greyscale the input image and use Hog to select polymorphic pedestrian target features in public places. Then, calculate the probability density of the target area model and construct a pedestrian target recognition and tracking model. Finally, extract the colour features of the target, select the Bhattacharyya coefficient to calculate the similarity between the target model and the candidate model, and use the camshift algorithm for target recognition, tracking, and matching to obtain the final recognition and tracking results. The experimental results show that the accuracy of the proposed method can reach 97.78 and the operation time is only 0.082 frames/s, indicating that the proposed method effectively improves the target recognition and tracking performance.
Automated guided vehicles (AGVs) are Internet of Things robots that navigate automatically as guided by a central control platform with distributed intelligence. Different methodologies have been proposed for AGV visu...
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Automated guided vehicles (AGVs) are Internet of Things robots that navigate automatically as guided by a central control platform with distributed intelligence. Different methodologies have been proposed for AGV visual tracking applications. However, vision-based tracking in AGVs usually confronts the problem of time delay caused by the complexity of image processing algorithms. To balance the trade-off among algorithm complexity, hardware cost and performance, precision and robustness are usually compromised in practical deployment. This paper proposes a prototype design of a visual tracking system. Edge computing is implemented which migrates computation intensive image processing to a local computer. The Raspberry Pi-based AGV captures the real-time image through the camera, sends the images to the computer and receives the processing results through the WiFi link. An improved camshift algorithm is developed and implemented. Based on this algorithm, the AGV can make convergent prediction of the pixels in the target area after the first detection of the object. Relative coordinates of the target can be located more accurately in less time. As tested in the experiments, the system architecture and new algorithm lead to reduced hardware cost, less time delay, improved robustness and higher accuracy in tracking.
Aiming at the problem of target loss caused by background interference and occlusion in the traditional camshift algorithm for target tracking, a camshift tracking algorithm based on feature matching and prediction me...
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ISBN:
(纸本)9781665441018
Aiming at the problem of target loss caused by background interference and occlusion in the traditional camshift algorithm for target tracking, a camshift tracking algorithm based on feature matching and prediction mechanism is designed. The algorithm re-locates the size and position of the target by ORB feature matching between the template target and the frame to be tracked, and realizes the accurate tracking of the target under the interference of similar background and complex background. In order to solve the problem of inaccurate tracking and feature matching under occlusion, the Kalman filter is used to predict the position of the occluded target. The experimental results show that the algorithm can accurately track the target under complex background, similar background and occlusion.
This study investigates the use of the continuously adaptive mean shift (camshift) algorithm in high-speed vision extraction. Videos of a high-speed target are captured with a high-speed camera. The displacement of th...
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This study investigates the use of the continuously adaptive mean shift (camshift) algorithm in high-speed vision extraction. Videos of a high-speed target are captured with a high-speed camera. The displacement of the target is extracted from the videos using the camshift algorithm. The camshift algorithm is then compared with the normalized cross correlation algorithm and other algorithms in terms of accuracy and rapidity. Simulation and test results indicate that the camshift algorithm is better than other algorithms in terms of the displacement extraction of high-frequency vibration. The camshift algorithm also offers numerous advantages, including real time, high efficiency, high accuracy, and robustness.
Moving target location and tracking is a very complicated problem, especially, the occlusion problem gradually becomes the limitation of the practical tracking algorithm. Because of the moving targets in video monitor...
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ISBN:
(数字)9781538682463
ISBN:
(纸本)9781538682463
Moving target location and tracking is a very complicated problem, especially, the occlusion problem gradually becomes the limitation of the practical tracking algorithm. Because of the moving targets in video monitoring can be occluded by the obstacles in the moving process, it can reduce target positioning accuracy, and even lead to the target track losing. To resolve the problems, a new location algorithm has been proposed in this paper. The preprocessing of video image is realized through the basic process of Gauss foreground detection, Kalman filter, expansion and occlusion judgment. And then, the method based on the Kalman prediction and the adaptive threshold value which determined by the Euclidean distance of the target motion trajectory, and the target centroids have been searched by camshift algorithm. The test results show that the method of using the Euclidean distance of the target trajectory as the prediction threshold can not only effectively locate the moving target, but also resist to the adverse effects of the occlusion object, and reduce the loss rate of tracking.
There are three critical elements in the visible light positioning (VLP) system: Positioning accuracy, real-time ability and robustness. However, few existing VLP studies consider these three critical elements at the ...
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There are three critical elements in the visible light positioning (VLP) system: Positioning accuracy, real-time ability and robustness. However, few existing VLP studies consider these three critical elements at the same time. Especially, robustness is usually ignored in VLP system, which has a great influence on positioning performance or even leads to the failure of positioning. Therefore, we propose a novel VLP method based on image sensor (as positioning terminal), using improved camshift-Kalman algorithm. The proposed algorithm not only combines camshift algorithm with Kalman filter, but also introduces the Bhattacharyya coefficient innovatively. It can realize high positioning accuracy, good robustness and good real-time ability. Experiments showed that the positioning accuracy of our proposed VLP algorithm was 0.55 cm, which realized high positioning accuracy. Besides, the average processing time per frame was 27.2 ms, which realized good real-time ability. Also, whether the LED was shielded, the interference occurred or the target's velocity changed suddenly, the proposed algorithm still maintained good positioning effect, which showed that it had good robustness.
To take advantage of the speed advantage of the camshift and try to overcome the problem of its poor robustness in target tracking, in this paper, a real time target face tracking algorithm based on saliency detection...
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To take advantage of the speed advantage of the camshift and try to overcome the problem of its poor robustness in target tracking, in this paper, a real time target face tracking algorithm based on saliency detection and camshift is proposed. Considering that the target to be tracked is more significant than the background in the frame, the saliency detection algorithm MBplus is first used to remove the background around the target as much as possible, so as to reduce the interference caused by the background to the camshift tracking results. Then the camshift is used to search and localize the targets in the processed video frames. At the same time, to compensate for the lack of tracking ability of camshift for some characteristic targets, the Kalman filter is used to predict the position of the target in the current frame. Finally, the Kalman-predicted target position, the target position obtained by camshift, are compared with the target tracked in the previous frame, and the position with high similarity is considered as the target tracking result of this paper. The experimental results show that the average tracking precision of the proposed target face tracking algorithm on the Birchfield database is 94.0%, its average tracking success rate on the NRC-IIT Facial Video Database is 100%, and even for the target faces with few attributes in ytcelebrity database, its tracking precision and tracking success rate are all 100%, which are superior to some state-of-the-art tracking algorithms.
camshift algorithm and three frame difference algorithm are the popular target recognition and tracking methods. camshift algorithm requires a manual initialization of the search window, which needs the subjective err...
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ISBN:
(纸本)9781510600539
camshift algorithm and three frame difference algorithm are the popular target recognition and tracking methods. camshift algorithm requires a manual initialization of the search window, which needs the subjective error and coherence, and only in the initialization calculating a color histogram, so the color probability model cannot be updated continuously. On the other hand, three frame difference method does not require manual initialization search window, it can make full use of the motion information of the target only to determine the range of motion. But it is unable to determine the contours of the object, and can not make use of the color information of the target object. Therefore, the improved camshift algorithm is proposed to overcome the disadvantages of the original algorithm, the three frame difference operation is combined with the object's motion information and color information to identify the target object. The improved camshift algorithm is realized and shows better performance in the recognition and tracking of the target.
camshift algorithm tracking is susceptible to interference when a tracking object is occluded or when its hue is similar to the background. An improved camshift object-tracking algorithm combining AKAZE (Accelerated-K...
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camshift algorithm tracking is susceptible to interference when a tracking object is occluded or when its hue is similar to the background. An improved camshift object-tracking algorithm combining AKAZE (Accelerated-KAZE) feature matching and Kalman filtering is proposed. First, the video channel is converted for processing. Second, AKAZE is used to match the object feature points and Kalman filtering is used to predict the next position. Then different scenes are judged by the threshold and the camshift and Kalman tracking algorithms are used for object tracking, respectively. Finally, the improved camshift algorithm is used to test the moving object in a variety of situations and compared with the traditional camshift algorithm and the Kalman filter improved camshift algorithm. Experimental results show that the improved joint tracking algorithm can continue tracking under full occlusion. The effective frame rate of recognition is increased by about 20%, and the single-frame image processing time is less than 35 ms, which can meet the real-time tracking requirements.
For the traditional moving target tracking algorithm, it is difficult to detect the target area accurately because of the influence of environmental factors. Therefore, manual selection of moving targets is often used...
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For the traditional moving target tracking algorithm, it is difficult to detect the target area accurately because of the influence of environmental factors. Therefore, manual selection of moving targets is often used, but this method is not intelligent enough, and has strong subjectivity and experience, so the data obtained is not convincing enough. For this reason, based on the multi-frame subtraction method, the connected region search is used to select the appropriate moving target and calibrate the target area, and then the moving target is determined and tracked. At the target tracking stage, the fusion feature vectors in the calibrated area are updated in real time to improve the accuracy and robustness of the follow-up tracking algorithm. Experiments show that the method is feasible. In the stage of target detection, the improved algorithm filters and calibrates the target area on the premise of increasing a small amount of computation. In the stage of target tracking, the computational area is reduced to the calibration and tracking range, which improves the efficiency of the algorithm and guarantees the stability and robustness of the algorithm.
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