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IEEE Canadian Conference on Electrical and Computer Engineering

作者： Ning Li Dan Zhang Xiaorong Gu Li Huang Wei Liu Tao Xu Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education and College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China College of Science Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China Information Technology Research Base of Civil Aviation Administration of China Civil Aviation University of China Tianjin 300300 P. R. China

ISBN: (纸本)9781479958306

Moving object tracking is one of the key technologies in video surveillance. mean shift algorithm fails to track the moving object in complicated environment. In this paper, a new strategy is proposed to improve the tracking ability of mean shift algorithm, in which the contrast between object and background along with similarity evaluation are applied for generating and updating object model. To eliminate the interference of the most similar features between tracking object and background, the coefficient ratio of the object to surrounding environment is first imported to generate the object model. To make sure the accuracy of updating object model, the effective way that combines similarity evaluation and Kalman filtering prediction is then applied for judge whether the tracking object is sheltered by other objects or background. The experimental results have shown that the proposed method can tack the moving object stably.

关键词： moving object tracking video surveillance object model generation mean shift algorithm Kalman filtering prediction

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Multiple Objects Tracking Using Extended Kalman Filter, GMM and mean shift algorithm - A comparative Study

Multiple Objects Tracking Using Extended Kalman Filter, GMM ...

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International Conference on Advanced Communication Control and Computing Technologies

作者： D. Harihara Santosh P.G. Krishna Mohan JNTU College of Engineering Hyderabad Andhra Pradesh Department of ECE Institute of Aeronautical EngineeringHyderabad Andhra Pradesh

ISBN: (纸本)9781479939152

Object tracking is a primary step for image processing applications like object recognition, navigation systems and surveillance systems. The current image and the background image is differentiated by approaching conventionally in image processing. Image subtraction based algorithms are mainly used in extracting features of moving objects and take the information in frames. Here three algorithms namely Extended Kalman Filter, Gaussian Mixture Model (GMM), mean shift algorithm are compared in the context of multiple object tracking. The comparative results show that GMM performs well when there are occlusions. Extended Kalman filter fails because of abnormal behavior in the distribution of random variables when there is nonlinear transformation. It cannot identify multiple objects when there are occlusions. mean shift algorithm is best suitable for single object tracking and is very sensitive to window size which is adaptive. Results show that this algorithm has the limitation to detect multiple objects when there is even slight occlusion.

关键词： Gaussian mixture model Extended Kalman Filter mean shift algorithm multiple object tracking background subtraction foreground detection

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Study on mean shift tracking algorithm combined with tracking differentiator

Study on mean shift tracking algorithm combined with trackin...

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International Symposium on Optoelectronic Technology and Application (IPTA) - Image Processing and Pattern Recognition

作者： Huang, Chen Wang, Jianjun Xue, Li Gao, Xin Beijing Inst Tracking & Telecommun Technol Beijing Peoples R China

ISBN: (纸本)9781628413878

The classical mean shift algorithm for target tracking, when it is used to track window in a complex background, appears too vulnerable to avoid window jitter, which leads to a failure of accurately tracking the target;while in tracking the target of fast-moving video sequences, the loss of tracking targets can not be avoid. As an improvement based on the classical mean shift algorithm, the proposed algorithm with characteristic of combining tracking differentiator (TD) can eliminate window jitter and predict the target location with the help of TD. Experiments demonstrate that the proposed algorithm is capable of getting over deficiency of the original algorithm and holding improved stability and robustness.

关键词： target tracking mean shift algorithm tracking differentiator tracking window filter

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An Improved mean - shift algorithm with Self-Scaling Tracking Window

An Improved Mean - Shift Algorithm with Self-Scaling Trackin...

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3rd International Conference on Computing, Communication, Control and Automation (ICCUBEA)

作者： Mahajan, Priya K. Dewasthale, Mugdha M. MKSSSs Cummins Coll Engn Women Dept Elect & Telecommun Pune Maharashtra India

ISBN: (纸本)9781538640081

In standard mean shift based object tracking algorithm, kernel bandwidth (BW) is fixed due to which object tracking fails if object is changed in the size. To overcome this problem, this paper proposes the method to increase tracking potential of the standard mean-shift algorithm (MSA). Firstly, it calculates the size of the object, if it changes then finds the size of kernel-BW. Accordingly, it adjusts the kernel-BW of the MSA. Finally, using improved MSA, the tracking object has been accurately located with proper kernel-BW. The experimental result of the proposed algorithms has been proved that it is improved than the existing standard mean shift algorithm.

关键词： mean shift algorithm self-scaling video monitoring Moving vehicle tracking

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Plant Leaves Edge Detection Using mean-shift algorithm and Wavelet Transform

Plant Leaves Edge Detection Using Mean-shift Algorithm and W...

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2016 International Conference on Computational Modeling, Simulation and Applied Mathematics (CMSAM 2016)

作者： Yang KUANG Xin WANG Institute of Education Jiangxi Science and Technology Normal University

In this paper, a novel edge detection algorithm based on mean-shift algorithm and wavelet transform is proposed to accurately segment the Leaves in the Leaf overlapped and grain complex situations. Firstly, mean-shift algorithm of adaptive spatial bandwidth selection was put forward to smooth the plants leaves image Secondly two-dimensional wavelet transform was utilized to enhance contrast ratio. Then the edge of the plant leaf was lastly detected based on Canny operator. Compared with traditional edge detection algorithms, this edge detected by this method has a better continuity and has a higher signal-to-noise ratio.

关键词： mean shift algorithm Wavelet transform The spatial bandwidth The canny operator Edge detection

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The Improvement of mean- shift algorithm and Kalman Filter of Tracking Moving Targets in the video of Robotic Fish

The Improvement of Mean- Shift Algorithm and Kalman Filter o...

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2015 4th International Conference on Mechatronics,Materials,Chemistry and Computer Engineering(ICMMCCE 2015)

作者： Jingying Wu Wei Xiang Institute of Electrical and Information Engineering Southwest University for Nationalities

ISBN: (纸本)9781510817371

Several defects have been found in the former experiments of basic mean shift algorithm, such as the erroneous judgments when target blocked by a large proportion of barrier. Thus, a fast target tracking algorithm based on mean shift combined Kalman filter is proposed for the theoretical defect of basic mean shift. The blocking issue and tracking of fast moving target are discussed in this paper and applied in the independent visual robotic fish tracking. Here are our conceptions: Firstly, the initial position of mean shift is provided with Kalman filter in every frame, then mean shift algorithm is used to track the position of target. Secondly, the calculation of the Kalman residuals is applied to turn on and turn off the Kalman filter under a large proportion of blocking situation, at the same time, the linear prediction of the target position is replaced by Kalman's function test. Finally, we conduct a real-time tracking experiment on the independent visual robotic fish tracking a moving ball, it is proved that the algorithm can achieve the tracking of fast moving target and robust against the barrier as well.

关键词： mean shift algorithm Kalman residuals Kalman filter Target tracking

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Implementation of the parallel mean shift-based imagesegmentation algorithm on a GPU cluster

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International Journal of Digital Earth 2019年第3期12卷 328-353页

作者： Fang Huang Yinjie Chen Li Li Ji Zhou Jian Tao Xicheng Tan Guangsong Fana School of Resources&Environment University of Electronic Science and Technology of ChinaChengduPeople’s Republic of China Institute of Remote Sensing Big Data Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China School of Construction&Management Engineering Xihua UniversityChengduPeople’s Republic of China Texas A&M Engineering Experiment Station and High Performance Research Computing Texas A&M UniversityCollege StationTXUSA International School of Software Wuhan UniversityWuhanPeople’s Republic of China

The mean shift image segmentation algorithm is very computationintensive. To address the need to deal with a large number of remotesensing (RS) image segmentations in real-world applications, this studyhas investigated the parallelization of the mean shift algorithm on asingle graphics processing unit (GPU) and a task-scheduling methodwith message passing interface (MPI)+OpenCL programming model on aGPU cluster platform. This paper presents the test results of the parallelmean shift image segmentation algorithm on Shelob, a GPU clusterplatform at Louisiana State University, with different datasets andparameters. The experimental results show that the proposed parallelalgorithm can achieve good speedups with different configurations andRS data and can provide an effective solution for RS image processingon a GPU cluster.

关键词： mean shift algorithm GPU cluster task scheduling MPI OpenCL

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A Hand Model Updating algorithm Based on mean shift

A Hand Model Updating Algorithm Based on Mean Shift

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4th International Conference on Information Computing and Applications (ICICA)

作者： Zou, Xiao Wang, Heng Duan, HongXiang Zhang, QiuYu Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China

ISBN: (纸本)9783642539329;9783642539312

To solve the problem of target model changing and influenced tracking results in gesture target tracking process, this paper proposed a gesture model updating and results forecasting algorithm based on mean shift. The algorithm firstly uses the background subtraction and skin color detection methods to detect and obtain gesture modeling, and then uses the mean shift algorithm to track gesture and update the gesture model, finally uses the Kalman algorithm to predict the gesture tracking results. The experimental results show that this algorithm reduces influence of surrounding environment in gesture tracking process, tracking effect is good.

关键词： mean shift algorithm Hand Tracking Gesture Model

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An Adaptive Tracking algorithm Based on mean shift

An Adaptive Tracking Algorithm Based on Mean shift

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2nd International Conference on Advanced Engineering Materials and Technology (AEMT)

作者： Deng, Zhenghong Li, Tingting Zhang, Tingting Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (纸本)9783037854471

Object tracking is to search the most similar parts to targets in video sequences. Among the various tracking algorithms, mean shift tracking algorithm has become popular due to its simplicity, efficiency and good performance. This paper focused on mean shift tracking algorithm, which is a modeling mechanism based on statistical probability density function. In practice, when the background of the tracking and characteristics of the target are similar, pixels of background occupy a large proportion in the histogram. The traditional mean shift cannot adapt to the mutative scene. meanwhile, if there is block or disappearance, the result is not exact. Three algorithms were given for above difficulties. A weighted template background was established, that can highlight the features of target and improve real-time. Then this paper presented a selective mechanism to update the target model. Every component is updated based on the contribution to the target model. Finally, the Kalman filter was combined with mean shift algorithm We saw the prediction points of Kalman filter as the initial point, carried out the mean shift iteration and then updated Kalman filter using the ultimate location. Extensive experimental results illustrated excellent agreement with these methods.

关键词： Tracking algorithm mean shift algorithm Kalman filter Target model

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A Novel Vehicle Tracking algorithm Based on mean shift and Active Contour Model in Complex Environment 2

A Novel Vehicle Tracking Algorithm Based on Mean Shift and A...

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Conference on Automated Visual Inspection and Machine Vision II

作者： Cai, Lei Wang, Lin Li, Bo Zhang, Libao Lv, Wen Minist Commun ITSC Res Inst Highway Key Lab Intelligent Transportat Syst Technol Beijing 100088 Peoples R China Beijing Normal Univ Coll Informat Sci & Technol 19 Xinjiekouwai Rd Beijing 100875 Peoples R China

ISBN: (纸本)9781510611146;9781510611139

Vehicle tracking technology is currently one of the most active research topics in machine vision. It is an important part of intelligent transportation system. However, in theory and technology, it still faces many challenges including real-time and robustness. In video surveillance, the targets need to be detected in real-time and to be calculated accurate position for judging the motives. The contents of video sequence images and the target motion are complex, so the objects can't be expressed by a unified mathematical model. Object-tracking is defined as locating the interest moving target in each frame of a piece of video. The current tracking technology can achieve reliable results in simple environment over the target with easy identified characteristics. However, in more complex environment, it is easy to lose the target because of the mismatch between the target appearance and its dynamic model. Moreover, the target usually has a complex shape, but the tradition target tracking algorithm usually represents the tracking results by simple geometric such as rectangle or circle, so it cannot provide accurate information for the subsequent upper application. This paper combines a traditional object-tracking technology, mean-shift algorithm, with a kind of image segmentation algorithm, Active-Contour model, to get the outlines of objects while the tracking process and automatically handle topology changes. meanwhile, the outline information is used to aid tracking algorithm to improve it.

关键词： Machine vision image processing target recognition object tracking mean shift algorithm active contour model

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