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23nd Signal Processing and Communications Applications Conference (SIU)

作者： Turhan, Mehmet Murat Hanbay, Davut Inonu Univ Bilgisayar Muhendisligi Bolumu Malatya Turkey

ISBN: (纸本)9781467373869

In this paper, mean shift algorithm and adaptive Kalman filter have been both utilized to realize object tracking in video sequences. mean shift algorithm cannot give good results when the position of the tracked object is changed rapidly between sequential frames or the tracked object is occluded. In this paper, the first position of the tracked object is predicted by Kalman filter then mean shift algorithm starts to seek the object in this position. Bhattacharyya coefficient which is obtained from mean shift algorithm, is used to instantly update Kalman filters error covariance matrix and determine whether object is occluded or not. Experimental results demonstrate that the proposed method has been more efficient technique as compared to standard mean shift algorithm in case of occlusion and fast object tracking.

关键词： Object Tracking mean shift algorithm Adaptive Kalman Filter

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Visual Tracking by Adaptive Kalman Filtering and mean shift

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6th Hellenic Conference on Artificial Intelligence

作者： Karavasilis, Vasileios Nikou, Christophoros Likas, Aristidis Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece

ISBN: (纸本)9783642128417

A method for object tracking combining the accuracy of mean shift with the robustness to occlusion of Kalman filtering is proposed. At first, an estimation of the object's position is obtained by the mean shift tracking algorithm and it is treated as the observation for a. Kalman filter. Moreover, we propose a dynamic scheme for the Kalman filter as the elements of its state matrix are updated on-line depending on a measure evaluating the quality of the observation. According to this measure, if the target is not occluded the observation contributes to the update equations of the Kalman filter state matrix. Otherwise, the observation is not taken into consideration. Experimental results show significant improvement with respect to the standard mean shift method both in terms of accuracy and execution time.

关键词： Visual tracking Kalman filter mean shift algorithm

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Automatic Detection and Tracking of Ship Based on mean shift in Corrected Video Sequences 2

Automatic Detection and Tracking of Ship Based on Mean Shift...

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2nd International Conference on Image, Vision and Computing (ICIVC)

作者： Chen, Zechuang Li, Bin Tian, Lian Fang Chao, Dong South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China South China Sea Marine Survey & Technol Ctr Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781509062386

Robust real-time ship detection and tracking for visual images automatically has become one of the crucial requirements for numerous situations. In order to improve the performance of automatic ship target detection and tracking system, a novel method based on mean shift is proposed for automatic detection and tracking of ship in the corrected video sequences. Firstly, video frames are corrected based on real-time attitude of the imaging equipment in order to decrease the drift of the ship target from frame to frame. Secondly, sea horizon is extracted based on Ostu algorithm and Hough transform. Thirdly, the ship location is detected based on the detection of grayscale peak. Finally, the ship is automatically tracked by using the mean shift algorithm. The result shows that this method has not only improved the robustness of the system against the shaking while tracking the ships, but also achieved a high success rate of tracking ships automatically in corrected video sequences.

关键词： image correction ship tracking mean shift algorithm image segmentation

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Improved mean-shift-based pitch determination

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Journal of Southeast University(English Edition) 2007年第4期23卷 494-499页

作者：吴红卫吴镇扬赵力东南大学信息科学与工程学院

The underlying principle of pitch determination based on the mean shift algorithm is studied, and the cause of pitch error propagation in the original pseudo code is analyzed. The problem of error propagation is solved by choosing an appropriate initial pitch candidate F00. The theoretical choice guideline in a pitch epoch is obtained as ensuring the true pitch F0 satisfying F00/2 〈 F0 〈 3F00/2. The validity of the choice guideline is verified by the F00 experiment. meanwhile, the algorithm is extended to the pitch determination in the noisy case and compared with the method of subharmonic-to-harmonic ratio （SHR）. The experimental results show that the improved algorithm bears comparison with SHR and it runs much faster than SHR.

关键词： pitch pitch determination mean shift algorithm

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Kernel smoothing, mean shift, and their learning theory with directional data

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 6833-6924页

作者： Yikun Zhang Yen-Chi Chen Department of Statistics University of Washington Seattle WA

Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science. This paper studies both statistical and computational problems of kernel smoothing for directional data. We generalize the classical mean shift algorithm to directional data, which allows us to identify local modes of the directional kernel density estimator (KDE). The statistical convergence rates of the directional KDE and its derivatives are derived, and the problem of mode estimation is examined. We also prove the ascending property of the directional mean shift algorithm and investigate a general problem of gradient ascent on the unit hypersphere. To demonstrate the applicability of the algorithm, we evaluate it as a mode clustering method on both simulated and real-world data sets.

关键词： directional data mean shift algorithm kernel smoothing mode clustering optimization on a manifold

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Gesture Tracking and Locating algorithm Based on Federated Tracking Filter

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2019年第12期141卷 1-6页

作者： Yu, Lei Hou, Junyi Fei, Shumin Soochow Univ Sch Mech & Elect Engn Suzhou 215006 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou 310027 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China

In this paper, a joint gesture tracking method combining particle filter and mean shift algorithm is proposed to improve the accuracy and robustness of the system. During the slow movement of the human hand, the average movement of the particles is first used to cause most of the particles to drift into the gesture area. In the case where the movement of the human hand is faster or there is occlusion, when the mean shift of the particle is performed, if the region of the gesture is not detected, the particle will return to the state before the drift, and then the next frame is processed. The method can directly calculate the position of the gesture based on the particles used for subsequent testing, and can save the tracking time of the algorithm. Through experimental simulation, compared with the Cam-shift algorithm, when the sampling point of the joint tracking algorithm proposed in this paper is 200, the tracking accuracy is improved to 95.2%. Compared with 90.6% of the Cam-shift algorithm, the tracking time is reduced from 83.7ms to 25.8ms. Therefore, the proposed algorithm can greatly improve the tracking accuracy and real-time, and can also effectively reduce the impact of complex environments on the tracking effect.

关键词： human-computer interaction federated tracking algorithm dynamic gesture tracking particle filter mean shift algorithm

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Anti-occlusion tracking algorithm of video target based on prediction and re-matching strategy

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2018年 57卷 176-182页

作者： Hu, Zhen-tao Zhou, Lin Yang, Ya-nan Liu, Xian-xing Jin, Yong Henan Univ Coll Comp & Informat Engn Kaifeng 475004 Peoples R China

Accurately locating the video target in the process of occlusion and recurrence will be very important for effective follow-up of the target For the problem of poor applicability of mean shift and its improved algorithm when the target is heavily occluded, this paper proposes an anti-occlusion video target tracking algorithm based on prediction and re-matching strategy. Firstly, dynamically combining the mean shift algorithm with the Kalman filter, this paper achieves stable tracking of un-occluded target. Secondly, when the target is occluded, Kalman filter is combined with the target prior information to predict the position of the occluded target. Finally, in the recurrence process of occluded targets, the target is re- matched through the normalized cross-correlation method to obtain target optimal position, and then the target can be quickly and accurately located. The simulation results show that the proposed method has strong anti-occlusion and reliability tracking in the video target tracking process. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Anti-occlusion mean shift algorithm Kalman filter Normalized cross correlation matching

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α-meanshift plus plus : Improving meanshift plus plus for Image Segmentation

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IEEE ACCESS 2021年 9卷 131430-131439页

作者： Park, Hanhoon Pukyong Natl Univ Dept Elect Engn Busan 43241 South Korea

meanshift is one of the popular clustering algorithms and can be used to partition a digital image into semantically meaningful regions in an unsupervised manner. However, due to its prohibitively high computational complexity, a grid-based approach, called meanshift++, has recently been proposed and succeeded to surprisingly reduce the computational complexity of meanshift. Nevertheless, we found that meanshift++ still has computational redundancy and there is room for improvement in terms of accuracy and runtime;thus, we propose an improvement to meanshift++, named alpha-meanshift++. We first attempt to minimize the computational redundancy by using an additional hash table. Then, we introduce a speedup factor (alpha) to reduce the number of iterations required until convergence, and we use more neighboring grid cells for the same bandwidth to improve accuracy. Through intensive experiments on image segmentation benchmark datasets, we demonstrate that alpha-meanshift++ can run 4.1-4.6 x faster on average (but up to 7 x) than meanshift CC and achieve better image segmentation quality.

关键词： Clustering mean shift algorithm meanshift plus image segmentation

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Multi-information incorporation approach to kernel-based infrared target model construction with application to target tracking

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OPTICAL ENGINEERING 2006年第11期45卷 110502-1-110502页

作者： Ling, Jianguo Liu, Erqi Yang, Lei Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China China Aerosp Sci & Ind Corp Inst Acad 2 Beijing 100854 Peoples R China

We present an approach that incorporates multi-information, including intensity value, spatial relation, and local standard deviation information of the pixels in target region, into kernel density estimation for constructing the kernel-based infrared (IR) target model. The incorporated information can complement each other for a target-tracking task. This constructed target model is evaluated based on the relative entropy of the two classes and is applied in a mean shift tracking system for IR target tracking to verify the effectiveness. (c) 2006 Society of Photo-Optical Instrumentation Engineers.

关键词： infrared target tracking target model kernel density estimation multi-information incorporation mean shift algorithm

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3-D mapping of a multi-layered Mediterranean forest using ALS data

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REMOTE SENSING OF ENVIRONMENT 2012年 121卷 210-223页

作者： Ferraz, Antonio Bretar, Frederic Jacquemoud, Stephane Goncalves, Gil Pereira, Luisa Tome, Margarida Soares, Paula Univ Paris Est Lab MATIS IGN F-94160 St Mande France Univ Paris Diderot Inst Phys Globe Paris Sorbonne Paris Cite CNRS UMR 7154 F-75013 Paris France Inst Engn Sistemas & Comp Coimbra P-3000033 Coimbra Portugal CETE Norrnandie Ctr Lab Ponts & Chaussees F-76121 Grand Quevilly France Univ Coimbra Dept Matemat P-3001454 Coimbra Portugal Univ Aveiro Escola Super Tecnol & Gestcio Agueda P-3754909 Agueda Portugal Univ Tecn Lisboa Inst Super Agron Ctr Estudos Florestais P-1349017 Lisbon Portugal

This study presents a robust approach for characterization of multi-layered forests using airborne laser scanning (ALS) data. Fuel mapping or biomass estimation requires knowing the diversity and boundaries of the forest patches, as well as their spatial pattern. This includes the thickness of the main vegetation layers, but also the spatial arrangement and size of the individual plants that compose each stratum. In order to decompose the ALS point cloud into genuine 3-D segments corresponding to individual vegetation features, such as shrubs or tree crowns, we apply a statistical approach based on the mean shift algorithm. The segments are progressively assigned to a forest layer: ground vegetation, understory or overstory. Our method relies on a single biophysically meaningful parameter, the kernel bandwidth, which is related to the local forest structure. It is validated on 44 plots of a Portuguese forest, composed mainly of eucalyptus (Eucalyptus globulus Labill.) and maritime pine (Pinus pinaster Ait.) trees. The number of detected trees varies with the dominance position: from 98.6% for the dominant trees to 12.8% for the suppressed trees. Linear regression models explain up to 70% of the variability associated with ground vegetation and understory height. (c) 2012 Elsevier Inc. All rights reserved.

关键词： Airborne laser scanning LiDAR Multi-layered forest Unsupervised segmentation mean shift algorithm Fuel mapping Vertical stratification Tree crown 3-D mapping Ground vegetation Understory Overstory

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