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Gotowe bibliografie tematyczne / Motion Estimation

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Gotowa bibliografia na temat „Motion Estimation”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Motion Estimation"

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Qin, Yongming, Makoto Kumon, and Tomonari Furukawa. "Estimation of a Human-Maneuvered Target Incorporating Human Intention." Sensors 21, no. 16 (2021): 5316. http://dx.doi.org/10.3390/s21165316.

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This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually constru

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Choi, Hee-Eun, and Jung-Il Jun. "Development of an Estimation Formula to Evaluate Mission Motion Suitability of Military Jackets." Applied Sciences 11, no. 19 (2021): 9129. http://dx.doi.org/10.3390/app11199129.

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We developed an estimation formula for mission motion suitability evaluation based on the general motion protocol to evaluate the motion suitability of a tracked vehicle crew jacket. Motion suitability evaluation was conducted for the 9 general motions and 12 mission motions among 27 tracked vehicle crew members who wore a tracked vehicle crew jacket. We conducted correlation and factor analyses on motions to extract the main mission motions, and a multiple regression analysis was performed on major mission motions using general motions as independent variables. As a result, two mission behavi

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Wan, Min, John J. Healy, and John T. Sheridan. "Fast 2D Subpixel Displacement Estimation." Photonics 11, no. 7 (2024): 625. http://dx.doi.org/10.3390/photonics11070625.

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Fast and simple methods for motion estimation with subpixel accuracy are of interest in a variety of applications. In this paper, we extend a recently proposed method for quantifying 1D displacements with subpixel accuracy, referred to as the subtraction method (SM) to 2D motion. Simulation and experimental results are presented. The results indicate that any general motion in 2D involving combinations of in-plane motions in x and y can be determined using SM after a 1D calibration. The errors between the actual motion and estimated are examined.

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Wang, Kaihong, Kumar Akash, and Teruhisa Misu. "Learning Temporally and Semantically Consistent Unpaired Video-to-Video Translation through Pseudo-Supervision from Synthetic Optical Flow." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2477–86. http://dx.doi.org/10.1609/aaai.v36i3.20148.

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Unpaired video-to-video translation aims to translate videos between a source and a target domain without the need of paired training data, making it more feasible for real applications. Unfortunately, the translated videos generally suffer from temporal and semantic inconsistency. To address this, many existing works adopt spatiotemporal consistency constraints incorporating temporal information based on motion estimation. However, the inaccuracies in the estimation of motion deteriorate the quality of the guidance towards spatiotemporal consistency, which leads to unstable translation. In th

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Li, Jiaman, C. Karen Liu, and Jiajun Wu. "Ego-Body Pose Estimation via Ego-Head Pose Estimation." AI Matters 9, no. 2 (2023): 20–23. http://dx.doi.org/10.1145/3609468.3609473.

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Estimating 3D human motion from an ego-centric video, which records the environment viewed from the first-person perspective with a front-facing monocular camera, is critical to applications in VR/AR. However, naively learning a mapping between egocentric videos and full-body human motions is challenging for two reasons. First, modeling this complex relationship is difficult; unlike reconstruction motion from third-person videos, the human body is often out of view of an egocentric video. Second, learning this mapping requires a large-scale, diverse dataset containing paired egocentric videos

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Zhou, Xuyang, Wei Jia, Ruofei He, and Wei Sun. "High-Precision Localization Tracking and Motion State Estimation of Ground-Based Moving Target Utilizing Unmanned Aerial Vehicle High-Altitude Reconnaissance." Remote Sensing 17, no. 5 (2025): 735. https://doi.org/10.3390/rs17050735.

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This paper focuses on the problem of ground-motion target localization tracking and motion state estimation for high-altitude reconnaissance using fixed-wing UAVs. Our goal is to accurately locate and track ground-moving targets and estimate their motion using visible light images, laser measurements of distance, and UAV position and attitude information. Firstly, this paper uses the target detection model of YOLOv8 to obtain the target pixel positions, combined with the measurement data, to establish the geolocalization model of the ground-motion target. Secondly, a motion state estimation al

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Popescu, Mihaela, Dennis Mronga, Ivan Bergonzani, Shivesh Kumar, and Frank Kirchner. "Experimental Investigations into Using Motion Capture State Feedback for Real-Time Control of a Humanoid Robot." Sensors 22, no. 24 (2022): 9853. http://dx.doi.org/10.3390/s22249853.

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Regardless of recent advances, humanoid robots still face significant difficulties in performing locomotion tasks. Among the key challenges that must be addressed to achieve robust bipedal locomotion are dynamically consistent motion planning, feedback control, and state estimation of such complex systems. In this paper, we investigate the use of an external motion capture system to provide state feedback to an online whole-body controller. We present experimental results with the humanoid robot RH5 performing two different whole-body motions: squatting with both feet in contact with the groun

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Ozawa, Takehiro, Yusuke Sekikawa, and Hideo Saito. "Accuracy and Speed Improvement of Event Camera Motion Estimation Using a Bird’s-Eye View Transformation." Sensors 22, no. 3 (2022): 773. http://dx.doi.org/10.3390/s22030773.

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Event cameras are bio-inspired sensors that have a high dynamic range and temporal resolution. This property enables motion estimation from textures with repeating patterns, which is difficult to achieve with RGB cameras. Therefore, motion estimation of an event camera is expected to be applied to vehicle position estimation. An existing method, called contrast maximization, is one of the methods that can be used for event camera motion estimation by capturing road surfaces. However, contrast maximization tends to fall into a local solution when estimating three-dimensional motion, which makes

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Phan, Gia-Hoang, Clint Hansen, Paolo Tommasino, Asif Hussain, Domenico Formica, and Domenico Campolo. "A Complementary Filter Design on SE(3) to Identify Micro-Motions during 3D Motion Tracking." Sensors 20, no. 20 (2020): 5864. http://dx.doi.org/10.3390/s20205864.

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In 3D motion capture, multiple methods have been developed in order to optimize the quality of the captured data. While certain technologies, such as inertial measurement units (IMU), are mostly suitable for 3D orientation estimation at relatively high frequencies, other technologies, such as marker-based motion capture, are more suitable for 3D position estimations at a lower frequency range. In this work, we introduce a complementary filter that complements 3D motion capture data with high-frequency acceleration signals from an IMU. While the local optimization reduces the error of the motio

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Jang, Jaeyoung, and Jong Hyeon Park. "A Method of Estimating an Object’s Parameters Based on Simplication with Momentum for a Manipulator." Applied Sciences 15, no. 7 (2025): 3989. https://doi.org/10.3390/app15073989.

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For a manipulator to estimate an object’s inertial parameters—such as mass, center of mass position, and elements of the inertia tensor—it must grasp one side of the object and generate motion while utilizing the resulting forces and torques at its end-effector for estimation. In most previous studies, the estimation motion has involved high acceleration, resulting in larger motion trajectories and increased inertial forces. A larger trajectory raises the risk of collisions, while greater inertial forces could potentially damage the object. This paper introduces an innovative approach that sim

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Rozprawy doktorskie na temat "Motion Estimation"

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Cheng, Xin. "Feature-based motion estimation and motion segmentation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0016/MQ55493.pdf.

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Jaganathan, Venkata Krishnan. "Robust motion estimation techniques." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/6032.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on April 15, 2008) Includes bibliographical references.

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Rekleitis, Ioannis. "Visual motion estimation based on motion blur interpretation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ44103.pdf.

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Rekleitis, Ioannis. "Visual motion estimation based on motion blur interpretation." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=20154.

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When the relative velocity between the different objects in a scene and the camera is relative large---compared with the camera's exposure time---in the resulting image we have a distortion called motion blur. In the past, a lot of algorithms have been proposed for estimating the relative velocity from one or, most of the time, more images. The motion blur is generally considered an extra source of noise and is eliminated, or is assumed nonexistent. Unlike most of these approaches, it is feasible to estimate the Optical Flow map using only the information encoded in the motion blur. This thesi

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Ay, Emre. "Ego-Motion Estimation of Drones." Thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210772.

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To remove the dependency on external structure for drone positioning in GPS-denied environments, it is desirable to estimate the ego-motion of drones on-board. Visual positioning systems have been studied for quite some time and the literature on the area is diligent. The aim of this project is to investigate the currently available methods and implement a visual odometry system for drones which is capable of giving continuous estimates with a lightweight solution. In that manner, the state of the art systems are investigated and a visual odometry system is implemented based on the design deci

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Wu, Siu Fan. "General motion estimation and segmentation." Thesis, University of Surrey, 1990. http://epubs.surrey.ac.uk/843155/.

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In this thesis, estimation of motion from an image sequence is investigated. The emphasis is on the novel use of motion model for describing two dimensional motion. Special attention is directed towards general motion models which are not restricted to translational motion. In contrast to translational motion, the 2-D motion is described by the model using motion parameters. There are two major areas which can benefit from the study of general motion model. The first one is image sequence processing and compression. In this context, the use of motion model provides a more compact description o

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Fakhouri, Elie Michel. "Variable block-size motion estimation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ37260.pdf.

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Mok, Wai-hung Toby, and 莫偉雄. "Motion estimation in feature domain." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31223230.

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Zayas-Cedeño, Gricelis 1974. "Motion estimation of cloud images." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50035.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (leaves 63-64).<br>by Gricelis Zayas-Cedeño.<br>M.S.

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Weiss, Yair. "Bayesian motion estimation and segmentation." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9354.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1998.<br>Includes bibliographical references (leaves 195-204).<br>Estimating motion in scenes containing multiple moving objects remains a difficult problem in computer vision yet is solved effortlessly by humans. In this thesis we present a computational investigation of this astonishing performance in human vision. The method we use throughout is to formulate a small number of assumptions and see the extent to which the optimal interpretation given these assumptions corresponds to the human percept.

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Książki na temat "Motion Estimation"

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Goh, Wooi Boon. Image motion estimation. typescript, 1992.

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Chakrabarti, Indrajit, Kota Naga Srinivasarao Batta, and Sumit Kumar Chatterjee. Motion Estimation for Video Coding. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14376-7.

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Ong, Ee Ping. Robust motion estimation and segmentation. University of Birmingham, 1997.

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Furht, Borko, Joshua Greenberg, and Raymond Westwater. Motion Estimation Algorithms for Video Compression. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6241-2.

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Furht, Borivoje. Motion estimation algorithms for video compression. Kluwer Academic Publishers, 1997.

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Furht, Borko. Motion estimation algorithms for video compression. Kluwer, 1997.

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Furht, Borivoje. Motion Estimation Algorithms for Video Compression. Springer US, 1997.

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Metkar, Shilpa, and Sanjay Talbar. Motion Estimation Techniques for Digital Video Coding. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1097-9.

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Ildiz, Faith. Estimation of motion parameters from image sequences. Naval Postgraduate School, 1991.

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Metkar, Shilpa. Motion Estimation Techniques for Digital Video Coding. Springer India, 2013.

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Części książek na temat "Motion Estimation"

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Gigengack, Fabian, Xiaoyi Jiang, Mohammad Dawood, and Klaus P. Schäfers. "Motion Estimation." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08392-6_2.

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Cuevas, Erik, Valentín Osuna, and Diego Oliva. "Motion Estimation." In Evolutionary Computation Techniques: A Comparative Perspective. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51109-2_5.

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Mitchell, Joan L., William B. Pennebaker, Chad E. Fogg, and Didier J. LeGall. "Motion estimation." In MPEG Video Compression Standard. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-4587-7_13.

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De Oliveira, Jauvane C. "Motion Estimation." In Encyclopedia of Multimedia. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78414-4_115.

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Tschumperlé, David, Christophe Tilmant, and Vincent Barra. "Motion Estimation." In Digital Image Processing with C++. CRC Press, 2023. http://dx.doi.org/10.1201/9781003323693-8.

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Mitchell, Joan L., William B. Pennebaker, Chad E. Fogg, and Didier J. LeGall. "Motion estimation." In MPEG Video Compression Standard. Springer US, 1996. http://dx.doi.org/10.1007/0-306-46983-9_13.

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Szeliski, Richard. "Motion Estimation." In Texts in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-34372-9_9.

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Lin, Youn-Long Steve, Chao-Yang Kao, Huang-Chih Kuo, and Jian-Wen Chen. "Integer Motion Estimation." In VLSI Design for Video Coding. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0959-6_3.

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Lin, Youn-Long Steve, Chao-Yang Kao, Huang-Chih Kuo, and Jian-Wen Chen. "Fractional Motion Estimation." In VLSI Design for Video Coding. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0959-6_4.

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Szeliski, Richard. "Dense motion estimation." In Texts in Computer Science. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-935-0_8.

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Streszczenia konferencji na temat "Motion Estimation"

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Huang, Yao, Hongyun Xiong, Xiaohong Nian, and Xunhua Dai. "Motion Estimation Track: Multiple Object Tracking by Using Motion Estimation After Detection Failure." In 2024 6th International Conference on Robotics and Computer Vision (ICRCV). IEEE, 2024. http://dx.doi.org/10.1109/icrcv62709.2024.10758596.

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Song, Inpyo, Jongmin Lee, Moonwook Ryu, and Jangwon Lee. "Motion-Aware Heatmap Regression for Human Pose Estimation in Videos." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/138.

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We present an approach to solving 2D human pose estimation in videos. The problem of human pose estimation in videos differs from estimating human poses in static images since videos contain a lot of motion related information. Thus, we investigate how to utilize by the information of the human body movements across in a sequence of video frames for estimating human poses in videos. To do this, we introduce a novel heatmap regression method what we call motion-aware heatmap regression. Our approach computes motion vectors in joint keypoints from adjacent frames. We then design a new style of h

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Rajala, S. A., I. M. Abdelqadar, G. L. Bilbro, and W. E. Snyder. "Motion estimation optimization." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226203.

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Boltz, Sylvain, and Frank Nielsen. "Randomized motion estimation." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5652514.

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Illgner, Klaus, Werner Praefcke, and Frank Mueller. "Multiresolution motion estimation." In Electronic Imaging: Science & Technology, edited by Robert L. Stevenson and M. Ibrahim Sezan. SPIE, 1996. http://dx.doi.org/10.1117/12.234738.

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Nalasani, Mahesh, W. David Pan, and Seong-Moo Yoo. "Motion estimation with integrated motion models." In the 42nd annual Southeast regional conference. ACM Press, 2004. http://dx.doi.org/10.1145/986537.986653.

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Liu Jin, Liu Jin, Ai Xiaofeng Ai Xiaofeng, Zhao Feng Zhao Feng, and Wu Qihua Wu Qihua. "Motion estimation of micro-motion targets with translational motion." In IET International Radar Conference 2015. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/cp.2015.1435.

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Lei, Meng, and Li Hang. "Motion Estimation Algorithm Based on Motion Characteristics." In 2009 WASE International Conference on Information Engineering (ICIE). IEEE, 2009. http://dx.doi.org/10.1109/icie.2009.40.

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Baytas, Inci M., and Bilge Gunsel. "Head motion classification with 2D motion estimation." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830231.

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Sun, Shijun, and Shawmin Lei. "Predictive motion estimation with global motion predictor." In Electronic Imaging 2004, edited by Sethuraman Panchanathan and Bhaskaran Vasudev. SPIE, 2004. http://dx.doi.org/10.1117/12.525114.

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Raporty organizacyjne na temat "Motion Estimation"

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Koc, Ut-Va, and K. J. Liu. DCT-Based Motion Estimation. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada452980.

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Gharavi, H., and H. Reza-Alikhani. Pel-recursive motion estimation algorithm. National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.6822.

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Alon, Jonathan, and Stan Sclaroff. Recursive Estimation of Motion and Planer Structure. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada451473.

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Koc, Ut-Va, and K. J. Liu. Exact Subpixel Motion Estimation in DCT Domain. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada455590.

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McCallen, D., and S. Larsen. Nevada - A Simulation Environment for Regional Estimation of Ground Motion and Structural Response. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15004876.

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Turner, Daniel Z. An overview of the gradient-based local DIC formulation for motion estimation in DICe. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1561808.

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Mojidra, Rushil, and Keri Ryan. Influence of Vertical Ground Motion on Bridges Isolated with Spherical Sliding Bearings. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2019. http://dx.doi.org/10.55461/rynq3624.

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The motivation for this project developed from testing of a full scale building isolated with triple friction pendulum bearings on the E-defense shake table in Japan. The test demonstrated experimentally that the vertical component of ground motion can amplify both the base shear and the story acceleration in the isolated building. Vertical shaking introduced high-frequency variation in the axial force of the bearings, and, consequently, a high-frequency component in the bearing lateral force, which excited higher structural modes in the building. Since vertical bridges are flexible in the ver

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Koschmann, Anthony, and Yi Qian. Latent Estimation of Piracy Quality and its Effect on Revenues and Distribution: The Case of Motion Pictures. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27649.

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Gray, A. L., K. E. Mattar, P. W. Vachon, et al. InSAR Results from the RADARSAT Antarctic Mapping Mission Data: Estimation of Glacier Motion Using a Simple Registration Procedure. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219342.

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Soumekh, Mehrdad. Moving Target Detection and Motion Estimation in Foliage Using along Track Monopulse Synthetic Aperture Radar Imaging and Signal Subspace Processing of Uncalibrated MTD-SARs. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada329234.

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