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Vehicle Flow Counting System Based on Traffic Surveillance Video

电脑学刊 2019年第4期30卷 185-192页

作者： Fu-Juan Xu Bo-Shen Ya-Juan Wang Ying-Ji Liu

With the rapid development of intelligent traffic surveillance systems, traffic monitoring has become automatic and intelligent. Traffic flow detection is an important part of Intelligent Transportation System (ITS), and it plays an important role in traffic management and monitoring. In this paper, we propose and implement a traffic surveillance system for vehicle flow detection and counting. The system we proposed consists of three parts: target detection, target tracking and target counting. Based on background modeling, we detect the foreground target and mean-shift color histogram matching algorithm has been adopted to track the target. Then we set the detection line to count the target in the region of interest (ROI). The experiment results show that our proposed system has good performance and high accuracy in real traffic video.

关键词： mean-shift algorithm target detection target tracking vehicle flow virtual detection line

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Image Segmentation Using Linked mean-shift Vectors and Global/Local Attributes

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2017年第10期27卷 2132-2140页

作者： Cho, Hanjoo Kang, Suk-Ju Kim, Young Hwan Pohang Univ Sci & Technol Dept Elect Engn Pohang 790784 South Korea Sogang Univ Dept Elect Engn Seoul 121742 South Korea

This paper proposes novel noniterative mean-shift-based image segmentation that uses global and local attributes. The existing mean-shift-based methods use a fixed range bandwidth, and hence their accuracy is dependent on the range spectrum of an image. To resolve this dependency, this paper proposes to modify the range kernel in the mean-shift process to be anisotropic. The modification is conducted using a global attribute defined as the range covariance matrix of the image. Further, to alleviate oversegmentation, the proposed method merges the segments having similar local attributes more aggressively than other segments. The local attribute for each segment is defined as the sum of the variances of the chromatic components. Finally, to expedite the processing, the proposed method uses a region adjacency graph (RAG) for the merging process, thus differing from the existing linked mean-shift-based methods. In the experiments on the Berkeley segmentation data set, the use of the global and local attributes improved segmentation accuracy;the proposed method outperformed the state-of-the-art linked mean-shift-based method by showing an improvement of 2.15%, 3.16%, 3.32%, and 1.90% in probability rand index, segmentation covering, variation of information, and F-measure, respectively. Further, compared with the benchmark method, which uses the dilating and merging scheme, the proposed method improved the speed of the merging process 42 times by applying the RAG.

关键词： Computer vision image analysis mean-shift algorithm

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A Combined Back and Foreground-Based Stereo Matching algorithm Using Belief Propagation and Self-Adapting Dissimilarity Measure

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2018年第6期32卷 1850019-1850019页

作者： Wang, Xiaofeng Su, Yingying Tang, Liming Tan, Jie Chongqing Univ Sci & Technol Coll Math & Phys Sci Chongqing 401331 Peoples R China Chongqing Univ Sci & Technol Coll Elect & Informat Engn Chongqing 401331 Peoples R China Hubei Univ Nationalities Sch Sci Enshi 445000 Peoples R China

Belief propagation (BP) algorithm still exists some shortages, such as inaccurate edge preservation and ambiguous detail information in the foreground, while self-adapting dissimilarity measure (SDM) also exists some shortages, such as ill textureless and occluded information in the background. To address these problems, we present a novel stereo matching algorithm fusing BP and SDM with an excellent background and foreground information. Lots of experiments show that BP and SDM can complement each other. BP algorithm can hold the better background information due to message propagation inference, whereas SDM can possess the better foreground information due to detail treatment. Therefore, a piecewise function is proposed, which can combine BP algorithm in an excellent background information and SDM in the foreground information, and greatly improve the disparity effect as a whole. We also expect that this work can attract more attention on combination of local methods and global methods, due to its simplicity, effciency, and accuracy. Experimental results show that the proposed method can keep the superior performance and hold better background and foreground on the Middlebury datasets, compared to BP and SDM.

关键词： Stereo matching belief propagation self-adapting dissimilarity measure mean-shift algorithm

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Automatic Nuclei Detection Based on Generalized Laplacian of Gaussian Filters

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2017年第3期21卷 826-837页

作者： Xu, Hongming Lu, Cheng Berendt, Richard Jha, Naresh Mandal, Mrinal Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA Univ Alberta Cross Canc Inst Dept Oncol Edmonton AB T6G 1Z2 Canada

Efficient and accurate detection of cell nuclei is an important step toward automatic analysis in histopathology. In this work, we present an automatic technique based on generalized Laplacian of Gaussian (gLoG) filter for nuclei detection in digitized histological images. The proposed technique first generates a bank of gLoG kernels with different scales and orientations and then performs convolution between directional gLoG kernels and the candidate image to obtain a set of response maps. The local maxima of response maps are detected and clustered into different groups by mean-shift algorithm based on their geometrical closeness. The point which has the maximum response in each group is finally selected as the nucleus seed. Experimental results on two datasets show that the proposed technique provides a superior performance in nuclei detection compared to existing techniques.

关键词： Laplacian of Gaussian (LoG) filter mean-shift algorithm nuclei detection thresholding

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Human Body Tracking System Based on DWT and mean-shift algorithm on ARM-Linux Platform

Human Body Tracking System Based on DWT and Mean-Shift Algor...

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World Congress on Information and Communication Technologies

作者： Ganesh Rakate Department of Instrumentation Engineering Vishwakarma Institute of Technology

ISBN: (纸本)9781467348065

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.

关键词： Human Body Tracking Discrete Wavelet Transform Spatial Information mean-shift algorithm ARM OpenCV Linux

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Interval-censored unimodal kernel density estimation via data sharpening

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2017年第10期87卷 2023-2037页

作者： Becker, Devan G. Braun, W. John White, Bethany J. G. Univ Western Ontario Stat & Actuarial Sci London ON Canada Univ British Columbia Okanagan Stat 1177 Res Rd Kelowna BC V1V 1V7 Canada Univ Toronto Stat Sci Toronto ON Canada

In this paper, we extend Choi and Hall's [Data sharpening as a prelude to density estimation. Biometrika. 1999;86(4):941-947] data sharpening algorithm for kernel density estimation to interval-censored data. Data sharpening has several advantages, including bias and mean integrated squared error (MISE) reduction as well as increased robustness to bandwidth misspecification. Several interval metrics are explored for use with the kernel function in the data sharpening transformation. A simulation study based on randomly generated data is conducted to assess and compare the performance of each interval metric. It is found that the bias is reduced by sharpening, often with little effect on the variance, thus maintaining or reducing overall MISE. Applications involving time to onset of HIV and running distances subject to measurement error are used for illustration.

关键词： Nonparametric estimation shape constraints mean-shift algorithm

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DESIGN AND DEVELOPMENT OF AN ON-BOARD AUTONOMOUS VISUAL TRACKING SYSTEM FOR UNMANNED AERIAL VEHICLES

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AVIATION 2017年第3期21卷 83-91页

作者： Kemsaram, Narsimlu Thatiparti, Venkata Rajini Kanth Guntupalli, Devendra Rao Kuvvarapu, Anil JNT Univ Dept Comp Sci & Engn Hyderabad 500085 Andhra Prades India SNIST Dept Comp Sci & Engn Hyderabad 501301 Andhra Prades India Cyient Ltd Dept Informat Syst Hyderabad 500081 Andhra Prades India Univ Michigan Dept Comp Sci Ann Arbor MI 48109 USA

This paper proposes the design and development of an on-board autonomous visual tracking system (AVTS) for unmanned aerial vehicles (UAV). A prototype of the proposed system has been implemented in MATLAB/ Simulink for simulation purposes. The proposed system contains GPS/INS sensors, a gimbaled camera, a multi-level autonomous visual tracking algorithm, a ground stationary target (GST) or ground moving target (GMT) state estimator, a camera control algorithm, a UAV guidance algorithm, and an autopilot. The on-board multi-level autonomous visual tracking algorithm acquires the video frames from the on-board camera and calculates the GMT pixel position in the video frame. The on-board GMT state estimator receives the GMT pixel position from the multi-level autonomous visual tracking algorithm and estimates the current position and velocity of the GMT with respect to the UAV. The on-board non-linear UAV guidance law computes the UAV heading velocity rates and sends them to the autopilot to steer the UAV in the desired path. The on-board camera control law computes the control command and sends it to the camera's gimbal controller to keep the GMT in the camera's field of view. The UAV guidance law and camera control law have been integrated for continuous tracking of the GMT. The on-board autopilot is used for controlling the UAV trajectory. The simulation of the proposed system was tested with a flight simulator and the UAV's reaction to the GMT was observed. The simulated results prove that the proposed system tracks a GST or GMT effectively.

关键词： autonomous visual tracking system aviator visual design simulator cam-shift algorithm extended Kalman filter estimator ground moving target ground stationary target mean-shift algorithm micro aerial vehicles unmanned aircraft systems unmanned aerial vehicles

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EVALUATION OF REAL-TIME HAND MOTION TRACKING USING A RANGE CAMERA AND THE mean-shift algorithm

EVALUATION OF REAL-TIME HAND MOTION TRACKING USING A RANGE C...

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ISPRS Workshop on Laser Scanning

作者： Lahamy, Herve Lichti, Derek Univ Calgary Dept Geomat Engn Calgary AB T2N 1N4 Canada

Several sensors have been tested for improving the interaction between humans and machines including traditional web cameras, special gloves, haptic devices, cameras providing stereo pairs of images and range cameras. meanwhile, several methods are described in the literature for tracking hand motion: the Kalman filter, the mean-shift algorithm and the condensation algorithm. In this research, the combination of a range camera and the simple version of the mean-shift algorithm has been evaluated for its capability for hand motion tracking. The evaluation was assessed in terms of position accuracy of the tracking trajectory in x, y and z directions in the camera space and the time difference between image acquisition and image display. Three parameters have been analyzed regarding their influence on the tracking process: the speed of the hand movement, the distance between the camera and the hand and finally the integration time of the camera. Prior to the evaluation, the required warm-up time of the camera has been measured. This study has demonstrated the suitability of the range camera used in combination with the mean-shift algorithm for real-time hand motion tracking but for very high speed hand movement in the traverse plane with respect to the camera, the tracking accuracy is low and requires improvement.

关键词： Hand motion tracking Range camera mean-shift algorithm Accuracy assessment

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mean-shift Moving Target Tracking algorithm Based on Weighted Sub-Block

Mean-Shift Moving Target Tracking Algorithm Based on Weighte...

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Proceedings of 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)

作者： Wang Xingmei Dong Hongbin Yang Xue Li Lin Harbin Engn Univ Comp Sci & Technol Coll Harbin Peoples R China Harbin Engn Univ Informat & Commun Engn Coll Harbin Peoples R China

ISBN: (纸本)9781479983759

To reduce the tracking errors caused by the background change and occlusion in dynamic scenes, a novel mean-shift moving target tracking algorithm based on weighted sub-block is proposed in this paper. Moving target tracking is completed by blocking the target region. The weight of each sub-block is determined by the combination of the similarity between target sub-block and candidate sub-block and the ratio of the target sub-block area and the overall area. At the same time, the edge position of the target sub-block is found by means of a Sobel operator edge detection algorithm. By which, the target subblock area is obtained. Both of the target region's RGB color information and the pixel's position information are taken into consideration while describing the characteristic model of target and candidate region inside each sub-block. The experiments demonstrate that the proposed method is insensitive to the background change and occlusion, and has better tracking performance with higher tracking accuracy and adaptability.

关键词： dynamic scene tracking moving target mean-shift algorithm

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Doppler Spectrum Segmentation of Radar Sea Clutter by mean-shift and Information Geometry Metric 17

Doppler Spectrum Segmentation of Radar Sea Clutter by Mean-S...

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17th International Radar Symposium (IRS)

作者： Barbaresco, Frederic Forget, Thibault Chevallier, Emmanuel Angulo, Jesus Thales Air Syst Adv Radar Concepts Business Unit Voie Pierre Gilles Gennes F-91470 Limours France Mines ParisTech Dept Math & Syst 35 Rue St Honore F-77300 Fontainebleau France

ISBN: (纸本)9781509025183

Radar sea clutter inhomogeneity in range is characterized by Doppler mean and spectrum width variations. We propose a new approach for robust statistical density estimation and segmentation of sea clutter Doppler spectrum. In each range cell, Doppler is characterized by a Toeplitz Hermitian Positive Definite covariance matrix that is coded in Poincare's unit poly-disk and we use adaptation of standard kernel methods to density estimation on this specific Riemannian manifold. Based on this non-parametric approach to estimate statistical density of Doppler Spectrum, we address the problem of sea clutter data mapping and segmentation by extending "mean-shift" tool for these densities on Poincare's unit poly-disk. This statistical segmentation is requested for robust detection of targets in sea clutter, especially in case of high sea state.

关键词： Terms Sea Clutter Fisher Metric Information Geometry Kernel Density Estimation mean-shift algorithm

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