The mean-shift based visual object tracking has achieved success in the field of computer vision because of its speediness and efficiency. It compute the features of object template and candidate regions by adopting t...
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ISBN:
(纸本)9781479942626
The mean-shift based visual object tracking has achieved success in the field of computer vision because of its speediness and efficiency. It compute the features of object template and candidate regions by adopting the weighted kernel based color histogram. However, the kernel-based color histogram may not have the ability to locate moving object accurately from the clutter background. In this paper, we propose a robust mean-shift object tracking algorithm based on weighted saliency. In order to increase discriminabiltity between object and background preferably and reduce the location error, the saliency of target and background is computed from the histogram bins. By incorporating the weighted saliency into Bhattacharyya similarity metric, an improved weighted background coefficient is defined based on the traditional mean-shift. The experiments and the comparison of tracking errors and correct tracking rate show that the effect of tracking is improved.
Computer vision gives computers the ability of visual perception similar to people, so that computers can feel the environment in the field of vision, understand the content of the feeling, and take corresponding acti...
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The tracking effect is not good for the faster track with meanshift tracking algorithm when the difference is not obvious between the track target and background pixels in the video of global visual robotic fish. To ...
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ISBN:
(纸本)9783037859711
The tracking effect is not good for the faster track with meanshift tracking algorithm when the difference is not obvious between the track target and background pixels in the video of global visual robotic fish. To solve the difficulty of tracking drastically moving targets in this paper, determining the position of moving targets in the next frame through comparing with two be coefficients which have been set when the Epanechnikov has been selected core to estimate is indeed. The experimental results show the proposed algorithm can track the moving targets efficiently and precisely in video, and also can meet high real-time situation with small calculation.
The rapid identification of planting patterns for major crops in a large irrigated district has vital importance for irrigation management,water fee collection,and crop yield *** this study,the OTSU algorithm and mean...
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The rapid identification of planting patterns for major crops in a large irrigated district has vital importance for irrigation management,water fee collection,and crop yield *** this study,the OTSU algorithm and mean-shift algorithm were employed to automatically determine threshold values for mapping two main rotated crop patterns at the pixel scale.A time series analysis was conducted to extract the spatial distribution of rice-wheat and wheat-maize rotations in the Chuanhang irrigation district from 2016 to *** results demonstrate that both threshold segmentation algorithms are reliable in extracting the spatial distribution of the crops,with an overall accuracy exceeding 80%.Additionally,both Kappa coefficients surpass 0.7,indicating better performance by OTSU *** the period from 2016 to 2020,the area occupied by rice-wheat rotation cropping ranged from 12500 to 14400 hm 2;whereas wheat-maize rotation cropping exhibited smaller and more variable areas ranging from 19730 to 34070 hm *** findings highlight how remote sensing-based approaches can provide reliable support for rapidly and accurately identifying the spatial distribution of main crop rotation patterns within a large irrigation district.
The mean-shift (MS) algorithm and its variants have wide applications in pattern recognition and computer vision tasks such as clustering, segmentation, and tracking. In this paper, we study the dynamics of the algori...
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The mean-shift (MS) algorithm and its variants have wide applications in pattern recognition and computer vision tasks such as clustering, segmentation, and tracking. In this paper, we study the dynamics of the algorithm with Gaussian kernels, based on a Generalized MS (GMS) model that includes the standard MS as a special case. First, we prove that the GMS has solutions in the convex hull of the given data points. By the principle of contraction mapping, a sufficient condition, dependent on a parameter introduced into Gaussian kernels, is provided to guarantee the uniqueness of the solution. It is shown that the solution is also globally stable and exponentially convergent under the condition. When the condition does not hold, the GMS algorithm can possibly have multiple equilibriums, which can be used for clustering as each equilibrium has its own attractive basin. Based on this, the condition can be used to estimate an appropriate parameter which ensures the GMS algorithm to have its equilibriums suitable for clustering. Examples are given to illustrate the correctness of the condition. It is also shown that the use of the multiple-equilibrium property for clustering, on the data sets such as IRIS, leads to a lower error rate than the standard MS approach, and the K-means and Fuzzy C-means algorithms. (c) 2012 Elsevier B.V. All rights reserved.
Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which i...
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Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the mean-shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the mean-shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.
Vehicle object tracking is a research hotspot in computer vision. To solve the problem of single object extraction caused by the shadow effect and occlusion between vehicles, this paper presents a vehicle object track...
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Vehicle object tracking is a research hotspot in computer vision. To solve the problem of single object extraction caused by the shadow effect and occlusion between vehicles, this paper presents a vehicle object tracking algorithm suitable for both dynamic and stationary states. First, the improved Canny algorithm is used to obtain the information in a video sequence, and the dynamic region of the object is extracted using the difference between the mean of the video sequence and the object frame. Secondly, the Gaussian mixture model is used for video object segmentation to obtain the foreground image and the background image, and the static object is identified through the intersection operation of the object dynamic region and the foreground image combined with the edge information. Then, chroma information is introduced into a statistical nonparametric model to eliminate the shadow of the foreground image, and the mean-shift tracking algorithm is used for dynamic object tracking of the foreground image after eliminating the shadow. The experimental results show that the proposed tracking algorithm can identify and track vehicles effectively and quickly, providing new ideas for the future development of the sensor field.
In order to improve the real-time character of missile radiator tracking and solve the predicting tracking problem when missile radiator shortly shelter or missing, it introduces moving target predicting and tracking ...
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ISBN:
(纸本)9783037852958
In order to improve the real-time character of missile radiator tracking and solve the predicting tracking problem when missile radiator shortly shelter or missing, it introduces moving target predicting and tracking technology. According to the predicting and tracking method, it proposes three predicting and tracking overall schemes of missile radiator based on Kalman filtering and improved mean-shift algorithm. Also it compares the real-time character of three kinds of schemes. According to the trajectory character of missile radiator, it constructs Kalman filter. The experiment results indicate that by using Kalman filtering technology, there are improvements in real-time character and shortly shelter or missing problem can be solved well. It plays a certain compensation function to the whole system.
A modified human body tracking system based on Discrete Wavelet Transform (DWT) and mean-shift algorithm is proposed. Most of the traditional object tracking systems have many disadvantages like complexity, high compu...
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ISBN:
(纸本)9781467348041
A modified human body tracking system based on Discrete Wavelet Transform (DWT) and mean-shift algorithm is proposed. Most of the traditional object tracking systems have many disadvantages like complexity, high computation power and large size. Here, whole system is implemented on ARM-Linux platform with camera mounted on rotary platform. DWT divides a frame into four different frequency bands without losing spatial information. So it neglects most of the fake motions in background as they are decomposed into high frequency wavelet sub-band. Color and spatial information are used as tracking parameters. mean-shift algorithm takes less number of calculations while converging to new object search window. Ultimate aim of this project is to implement single human body tracking system on ARM-Linux platform, for which minimum computations should be performed. Combination of DWT and mean-shift algorithm significantly decreases computation power. As shown in results, human body tracking system is successfully implemented.
In view of the phenomenon that the factors of occluded target and it's changed background lead to failure in the process of tracking, a MTF (mean-shift by TWH and FB-error) tracking algorithm is proposed. Firstly,...
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ISBN:
(纸本)9781509011445
In view of the phenomenon that the factors of occluded target and it's changed background lead to failure in the process of tracking, a MTF (mean-shift by TWH and FB-error) tracking algorithm is proposed. Firstly, Target-Weighted Histogram (TWH) is introduced to describe target in mean-shift tracking framework, i.e., using local-background of target to weaken inner-background features of all-region in order to make target features prominent in tracking process;secondly, FB-error restriction is introduced, the predicted results of target current position and the calculated results of mean-shift vector are combined together to estimate the final target location of time t by using weighted function about FB-error. The experimental results show that the proposed tracking algorithm has great breakthrough on tracking accuracy.
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