Academic literature on the topic 'Analysis of Motion Trajectories'
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Journal articles on the topic "Analysis of Motion Trajectories":
Song, Huan-Sheng, Sheng-Nan Lu, Xiang Ma, Yuan Yang, Xue-Qin Liu, and Peng Zhang. "Vehicle Behavior Analysis Using Target Motion Trajectories." IEEE Transactions on Vehicular Technology 63, no. 8 (October 2014): 3580–91. http://dx.doi.org/10.1109/tvt.2014.2307958.
Curiac, Daniel-Ioan, and Constantin Volosencu. "A generic method to construct new customized-shaped haotic systems using the relative motion concept." Nonlinear Analysis: Modelling and Control 21, no. 3 (May 20, 2016): 413–23. http://dx.doi.org/10.15388/na.2016.3.8.
Dong, Ran, and Soichiro Ikuno. "Biomechanical Analysis of Golf Swing Motion Using Hilbert–Huang Transform." Sensors 23, no. 15 (July 26, 2023): 6698. http://dx.doi.org/10.3390/s23156698.
Carroll, Mary, Katja Weimar, Monique Flecken, Monique Lambert, and Christiane von Stutterheim. "Tracing trajectories." Language, Interaction and Acquisition 3, no. 2 (December 19, 2012): 202–30. http://dx.doi.org/10.1075/lia.3.2.03car.
BENSON, NOAH C., and VALERIE DAGGETT. "WAVELET ANALYSIS OF PROTEIN MOTION." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 04 (July 2012): 1250040. http://dx.doi.org/10.1142/s0219691312500403.
Xiang Ma, F. Bashir, A. A. Khokhar, and D. Schonfeld. "Event Analysis Based on Multiple Interactive Motion Trajectories." IEEE Transactions on Circuits and Systems for Video Technology 19, no. 3 (March 2009): 397–406. http://dx.doi.org/10.1109/tcsvt.2009.2013510.
Leem, Seung-min, Hyeon-seok Jeong, and Sung-young Kim. "Remote Drawing Technology Based on Motion Trajectories Analysis." Journal of Korea Institute of Information, Electronics, and Communication Technology 9, no. 2 (April 30, 2016): 229–36. http://dx.doi.org/10.17661/jkiiect.2016.9.2.229.
Marin, Mihnea, Petre Cristian Copilusi, and Ligia Rusu. "Experimental Approach Regarding the Analysis of Human Complex Motions." Applied Mechanics and Materials 823 (January 2016): 119–24. http://dx.doi.org/10.4028/www.scientific.net/amm.823.119.
Roth, Bernard. "Finding Geometric Invariants From Time-Based Invariants for Spherical and Spatial Motions." Journal of Mechanical Design 127, no. 2 (March 1, 2005): 227–31. http://dx.doi.org/10.1115/1.1828462.
SHENGBO, LI, А. YU KORNEEV, WANG SICONG, and E. V. MISHCHENKO. "THE ANALYSIS OF THE TRAJECTORIES OF MOTION RIGID ROTOR IN THE CONICAL LIQUID FRICTION BEARINGS." Fundamental and Applied Problems of Engineering and Technology 6 (2020): 114–20. http://dx.doi.org/10.33979/2073-7408-2020-344-6-114-120.
Dissertations / Theses on the topic "Analysis of Motion Trajectories":
Partsinevelos, Panayotis. "Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery." Fogler Library, University of Maine, 2002. http://www.library.umaine.edu/theses/pdf/PartsinevelosP2002.pdf.
Chassat, Perrine. "Functional and Shape Data Analysis under the Frenet-Serret Framework : Application to Sign Language Motion Trajectories Analysis." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM005.
This thesis, conducted in collaboration with MocapLab, a company specializing in motion capture, aims to determine the optimal mathematical framework and relevant descriptors for analyzing sign language motion trajectories. Drawing on principles of motor control, we identified the framework defined by the Frenet-Serret formulas, including curvature, torsion, and velocity parameters, as particularly suitable for this task. By introducing new curve analysis approaches based on the Frenet framework, this thesis contributes to developing novel methods in functional data analysis and shape analysis. The first part of this thesis addresses the challenge of smoothly estimating Frenet curvature parameters, treating the problem as parameter estimation of differential equation in SO(d), (d ≥ 1). We introduce a functional Expectation-Maximization algorithm that defines a unified variable estimation method in the SE(3) group, providing smoother estimators that are more reliable and robust than existing methods. In the second part, two new curve representations are introduced: unparametrized Frenet curvatures and the Square Root Curvatures (SRC) transform, establishing new Riemannian geometric frameworks for smooth curves in ℝᵈ, (d ≥ 1). Leveraging higher-order geometric information and parametrization dependence, the Square Root Curvatures transform outperforms the state-of-the-art Square-Root Velocity Function (SRVF) representation on synthetic results. Given a collection of curves, this type of geometry allows us to define efficient statistical criteria for estimating Karcher mean shapes on the associated Riemannian shape spaces, proving particularly effective on noisy data. Finally, this developed framework opens the door to more practical applications in sign language processing, including the study of power laws on our data and the development of a generative model for a point motion in sign language
Jetchev, Nikolay N. [Verfasser]. "Learning representations from motion trajectories : analysis and applications to robot planning and control / Nikolay Nikolaev Jetchev." Berlin : Freie Universität Berlin, 2012. http://d-nb.info/1027151604/34.
Beaudry, Cyrille. "Analyse et reconnaissance de séquences vidéos d'activités humaines dans l'espace sémantique." Thesis, La Rochelle, 2015. http://www.theses.fr/2015LAROS042/document.
This thesis focuses on the characterization and recognition of human activities in videos. This research domain is motivated by a large set of applications such as automatic video indexing, video monitoring or elderly assistance. In the first part of our work, we develop an approach based on the optical flow estimation in video to recognize human elementary actions. From the obtained vector field, we extract critical points and trajectories estimated at different spatio-temporal scales. The late fusion of local characteristics such as motion orientation and shape around critical points, combined with the frequency description of trajectories allow us to obtain one of the best recognition rate among state of art methods. In a second part, we develop a method for recognizing complex human activities by considering them as temporal sequences of elementary actions. In a first step, elementary action probabilities over time is calculated in a video sequence with our first approach. Vectors of action probabilities lie in a statistical manifold called semantic simplex. Activities are then represented as trajectories on this manifold. Finally, a new descriptor is introduced to discriminate between activities from the shape of their associated trajectories. This descriptor takes into account the induced geometry of the simplex manifold
Almuhisen, Feda. "Leveraging formal concept analysis and pattern mining for moving object trajectory analysis." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738/document.
This dissertation presents a trajectory analysis framework, which includes both a preprocessing phase and trajectory mining process. Furthermore, the framework offers visual functions that reflect trajectory patterns evolution behavior. The originality of the mining process is to leverage frequent emergent pattern mining and formal concept analysis for moving objects trajectories. These methods detect and characterize pattern evolution behaviors bound to time in trajectory data. Three contributions are proposed: (1) a method for analyzing trajectories based on frequent formal concepts is used to detect different trajectory patterns evolution over time. These behaviors are "latent", "emerging", "decreasing", "lost" and "jumping". They characterize the dynamics of mobility related to urban spaces and time. The detected behaviors are automatically visualized on generated maps with different spatio-temporal levels to refine the analysis of mobility in a given area of the city, (2) a second trajectory analysis framework that is based on sequential concept lattice extraction is also proposed to exploit the movement direction in the evolution detection process, and (3) prediction method based on Markov chain is presented to predict the evolution behavior in the future period for a region. These three methods are evaluated on two real-world datasets. The obtained experimental results from these data show the relevance of the proposal and the utility of the generated maps
Almuhisen, Feda. "Leveraging formal concept analysis and pattern mining for moving object trajectory analysis." Electronic Thesis or Diss., Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738.
This dissertation presents a trajectory analysis framework, which includes both a preprocessing phase and trajectory mining process. Furthermore, the framework offers visual functions that reflect trajectory patterns evolution behavior. The originality of the mining process is to leverage frequent emergent pattern mining and formal concept analysis for moving objects trajectories. These methods detect and characterize pattern evolution behaviors bound to time in trajectory data. Three contributions are proposed: (1) a method for analyzing trajectories based on frequent formal concepts is used to detect different trajectory patterns evolution over time. These behaviors are "latent", "emerging", "decreasing", "lost" and "jumping". They characterize the dynamics of mobility related to urban spaces and time. The detected behaviors are automatically visualized on generated maps with different spatio-temporal levels to refine the analysis of mobility in a given area of the city, (2) a second trajectory analysis framework that is based on sequential concept lattice extraction is also proposed to exploit the movement direction in the evolution detection process, and (3) prediction method based on Markov chain is presented to predict the evolution behavior in the future period for a region. These three methods are evaluated on two real-world datasets. The obtained experimental results from these data show the relevance of the proposal and the utility of the generated maps
Khalid, Shehzad. "Motion classification using spatiotemporal approximation of object trajectories." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492915.
Sand, Peter (Peter M. ). 1977. "Long-range video motion estimation using point trajectories." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38319.
Includes bibliographical references (leaves 97-104).
This thesis describes a new approach to video motion estimation, in which motion is represented using a set of particles. Each particle is an image point sample with a long-duration trajectory and other properties. To optimize these particles, we measure point-based matching along the particle trajectories and distortion between the particles. The resulting motion representation is useful for a variety of applications and differs from optical flow, feature tracking, and parametric or layer-based models. We demonstrate the algorithm on challenging real-world videos that include complex scene geometry, multiple types of occlusion, regions with low texture, and non-rigid deformation.
by Peter Sand.
Ph.D.
Oliveira, Fábio Luiz Marinho de. "Video motion description based on histograms of sparse trajectories." Universidade Federal de Juiz de Fora (UFJF), 2016. https://repositorio.ufjf.br/jspui/handle/ufjf/4838.
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Descrição de movimento tem sido um tema desafiador e popular há muitos anos em visão computacional e processamento de sinais, mas também intimamente relacionado a aprendizado de máquina e reconhecimento de padrões. Frequentemente, para realizar essa tarefa, informação de movimento é extraída e codificada em um descritor. Este trabalho apresenta um método simples e de rápida computação para extrair essa informação e codificá-la em descritores baseados em histogramas de deslocamentos relativos. Nossos descritores são compactos, globais, que agregam informação de quadros inteiros, e o que chamamos de auto-descritor, que não depende de informações de sequências senão aquela que pretendemos descrever. Para validar estes descritores e compará-los com outros tra balhos, os utilizamos no contexto de Reconhecimento de Ações Humanas, no qual cenas são classificadas de acordo com as ações nelas exibidas. Nessa validação, obtemos resul tados comparáveis aos do estado-da-arte para a base de dados KTH. Também avaliamos nosso método utilizando as bases UCF11 e Hollywood2, com menores taxas de reconhe cimento, considerando suas maiores complexidades. Nossa abordagem é promissora, pelas razoáveis taxas de reconhecimento obtidas com um método muito menos complexo que os do estado-da-arte, em termos de velocidade de computação e compacidade dos descritores obtidos. Adicionalmente, experimentamos com o uso de Aprendizado de Métrica para a classificação de nossos descritores, com o intuito de melhorar a separabilidade e a com pacidade dos descritores. Os resultados com Aprendizado de Métrica apresentam taxas de reconhecimento inferiores, mas grande melhoria na compacidade dos descritores.
Motion description has been a challenging and popular theme over many years within computer vision and signal processing, but also very closely related to machine learn ing and pattern recognition. Very frequently, to address this task, one extracts motion information from image sequences and encodes this information into a descriptor. This work presents a simple and fast computing method to extract this information and en code it into descriptors based on histograms of relative displacements. Our descriptors are compact, global, meaning it aggregates information from whole frames, and what we call self-descriptors, meaning they do not depend on information from sequences other than the one we want to describe. To validate these descriptors and compare them to other works, we use them in the context of Human Action Recognition, where scenes are classified according to the action portrayed. In this validation, we achieve results that are comparable to those in the state-of-the-art for the KTH dataset. We also evaluate our method on the UCF11 and Hollywood2 datasets, with lower recognition rates, considering their higher complexity. Our approach is a promising one, due to the fairly good recogni tion rates we obtain with a much less complex method than those of the state-of-the-art, in terms of speed of computation and final descriptor compactness. Additionally, we ex periment with the use of Metric Learning in the classification of our descriptors, aiming to improve the separability and compactness of the descriptors. Our results for Metric Learning show inferior recognition rates, but great improvement for the compactness of the descriptors.
Chen, Ni. "Contouring control in high performance motion systems /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202005%20CHENN.
Books on the topic "Analysis of Motion Trajectories":
Center, Langley Research, and Georgia Institute of Technology. School of Aerospace Engineering., eds. Singular perturbation analysis of AOTV-related trajectory optimization problems. Atlanta, GA: Georgia Institute of Technology, School of Aerospace Engineering, 1990.
Tserpes, Konstantinos, Chiara Renso, and Stan Matwin, eds. Multiple-Aspect Analysis of Semantic Trajectories. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38081-6.
Mettrick, Christopher J. Analysis of the trajectories of miniature sonobuoys. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1991.
Workshop, on Visual Motion (1989 Irvine Calif ). Proceedings: Analysis, motion. Washington, D.C: IEEE Computer Society Press, 1989.
Z, Bober Miroslaw. Robust motion analysis. Baldock, Hertfordshire, England: Research Studies Press, 1999.
Aksu, Ibrahim. Performance analysis of image motion analysis algorithms. Monterey, Calif: Naval Postgraduate School, 1991.
Canada. Defence Research Establishment Atlantic. Ship Motion Analysis Program. S.l: s.n, 1986.
Sun, Yan. High-Orders Motion Analysis. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9191-4.
Grossman, Robert. The analysis of control trajectories using symbolic and database computing. [Washington, DC?: National Aeronautics and Space Administration, 1991.
Penn, Roger. Employment trajectories of Asian migrants in Rochdale: An integrated analysis. [London]: Economic and Social Research Council, 1990.
Book chapters on the topic "Analysis of Motion Trajectories":
Min, Junghye, Jin Hyeong Park, and Rangachar Kasturi. "Extraction of Multiple Motion Trajectories in Human Motion." In Image Analysis, 1050–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_138.
Jabłoński, Bartosz, and Marek Kulbacki. "Nonlinear Multiscale Analysis of Motion Trajectories." In Computer Vision and Graphics, 122–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15910-7_14.
Elaoud, Amani, Walid Barhoumi, Hassen Drira, and Ezzeddine Zagrouba. "Modeling Trajectories for 3D Motion Analysis." In Communications in Computer and Information Science, 409–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41590-7_17.
Weiss, Dieter G., Günther Galfe, Josef Gulden, Dieter Seitz-Tutter, George M. Langford, Albrecht Struppler, and Adolf Weindl. "Motion Analysis of Intracellular Objects: Trajectories with and without Visible Tracks." In Biological Motion, 95–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51664-1_7.
Buus, Ole Thomsen, Johannes Ravn Jørgensen, and Jens Michael Carstensen. "Analysis of Seed Sorting Process by Estimation of Seed Motion Trajectories." In Image Analysis, 273–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_26.
Del Bue, Alessio, Xavier Lladó, and Lourdes Agapito. "Segmentation of Rigid Motion from Non-rigid 2D Trajectories." In Pattern Recognition and Image Analysis, 491–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72847-4_63.
Demircan, Emel, Luis Sentis, Vincent De Sapio, and Oussama Khatib. "Human Motion Reconstruction by Direct Control of Marker Trajectories." In Advances in Robot Kinematics: Analysis and Design, 263–72. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8600-7_28.
Majerník, Jaroslav. "Reconstruction of Human Motion Trajectories to Support Human Gait Analysis in Free Moving Subjects." In Computational Intelligence, Medicine and Biology, 57–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16844-9_4.
Marteau, Pierre-François, and Sylvie Gibet. "Adaptive Sampling of Motion Trajectories for Discrete Task-Based Analysis and Synthesis of Gesture." In Lecture Notes in Computer Science, 224–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11678816_25.
Loseva, Elizaveta, Jaap van Krugten, Aniruddha Mitra, and Erwin J. G. Peterman. "Single-Molecule Fluorescence Microscopy in Sensory Cilia of Living Caenorhabditis elegans." In Single Molecule Analysis, 133–50. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3377-9_7.
Conference papers on the topic "Analysis of Motion Trajectories":
Devanne, Maxime, Hazem Wannous, Mohamed Daoudi, Stefano Berretti, Alberto Del Bimbo, and Pietro Pala. "Learning shape variations of motion trajectories for gait analysis." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899749.
Kihwan Kim, Dongryeol Lee, and Irfan Essa. "Gaussian process regression flow for analysis of motion trajectories." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126365.
Gesel, Paul, Momotaz Begum, and Dain La Roche. "Learning Motion Trajectories from Phase Space Analysis of the Demonstration." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794381.
Goto, Akihiko, Naoki Sugiyama, and Tomoko Ota. "Motion analysis of drone pilot operations and drone flight trajectories." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003749.
Ghaffari, Maryam, Yu-Fen Chang, Boris Balakin, and Alex C. Hoffmann. "CFD modeling of PEPT results of particle motion trajectories in a pipe over an obstacle." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756095.
Narayan, Sanath, and Kalpathi R. Ramakrishnan. "A Cause and Effect Analysis of Motion Trajectories for Modeling Actions." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.337.
Ayachi, Nimish, Piyush Kejriwal, Lalit Kane, and Pritee Khanna. "Analysis of the Hand Motion Trajectories for Recognition of Air-Drawn Symbols." In 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2015. http://dx.doi.org/10.1109/csnt.2015.95.
Seemann, Wolfgang, Gu¨nther Stelzner, and Christian Simonidis. "Correction of Motion Capture Data With Respect to Kinematic Data Consistency for Inverse Dynamic Analysis." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84964.
Cotton, R. James, Allison DeLillo, Anthony Cimorelli, Kunal Shah, J. D. Peiffer, Shawana Anarwala, Kayan Abdou, and Tasos Karakostas. "Optimizing Trajectories and Inverse Kinematics for Biomechanical Analysis of Markerless Motion Capture Data." In 2023 International Conference on Rehabilitation Robotics (ICORR). IEEE, 2023. http://dx.doi.org/10.1109/icorr58425.2023.10304683.
Ene, Nicoleta M., Florin Dimofte, and David A. Clark. "An Analysis of a Journal Bearing Sleeve Motion With a Transient Approach." In STLE/ASME 2010 International Joint Tribology Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ijtc2010-41183.
Reports on the topic "Analysis of Motion Trajectories":
Sharbaugh, R. C. Follow-up investigations of GPHS motion during heat pulse intervals of reentries from gravity-assist trajectories. Office of Scientific and Technical Information (OSTI), March 1992. http://dx.doi.org/10.2172/6365933.
Nevatia, Ram. Motion Analysis and its Applications. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada232945.
Lucero, E. F., and R. C. Sharbaugh. GPHS motion studies for heat pulse intervals of reentries from gravity-assist trajectories. Aerospace Nuclear Safety Program. Office of Scientific and Technical Information (OSTI), March 1990. http://dx.doi.org/10.2172/10149710.
Zhou, H. Numerical analysis of slender vortex motion. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/245550.
Lucero, E. F., and R. C. Sharbaugh. GPHS motion studies for heat pulse intervals of reentries from gravity-assist trajectories. [General Purpose Heat Source Module (GPHS)]. Office of Scientific and Technical Information (OSTI), March 1990. http://dx.doi.org/10.2172/6128798.
Rooks, Drew, and Trelanah McCalla. Human Dipping and Inserting Manipulation Motion Analysis. RPAL, December 2018. http://dx.doi.org/10.32555/2018.ir.001.
Foster, Michelle. MMWG Predictive Technologies - Case Study using Vibration Analysis, Phase Analysis, and Motion Amplification and other Motion Amplification Examples. Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1846901.
Sharbaugh, R. C. Follow-up investigations of GPHS motion during heat pulse intervals of reentries from gravity-assist trajectories. Aerospace Nuclear Safety Program. Office of Scientific and Technical Information (OSTI), March 1992. http://dx.doi.org/10.2172/10149701.
White, Jonathan R., and Damon J. Burnett. Analysis of Debris Trajectories at the Scaled Wind Farm Technology (SWiFT) Facility. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1235649.
Costeira, Joao, and Takeo Kanade. A Multi-Body Factorization Method for Motion Analysis,. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada295489.