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Опубліковано: 7 липня 2024
Оновлено: 7 липня 2024

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Статті в журналах з теми "Analysis of Motion Trajectories":

1

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.

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2

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.

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Constructing chaotic systems tailored for each particular real-world application has been a long-term research desideratum. We report a solution for this problem based on the concept of relative motion. We investigate the periodic motion on a closed contour of a coordinate frame in which a chaotic system evolves. By combining these two motions (periodic on a close contour and chaotic) new customized shape trajectories are acquired. We demonstrate that these trajectories obtained in the stationary frame are also chaotic and, moreover, conserve the Lyapunov exponents of the initial chaotic system. Based on this finding we developed an innovative method to construct new chaotic systems with customized shapes, thus fulfilling the requirements of any particular application of chaos.
3

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.

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In golf swing analysis, high-speed cameras and Trackman devices are traditionally used to collect data about the club, ball, and putt. However, these tools are costly and often inaccessible to golfers. This research proposes an alternative solution, employing an affordable inertial motion capture system to record golf swing movements accurately. The focus is discerning the differences between motions producing straight and slice trajectories. Commonly, the opening motion of the body’s left half and the head-up motion are associated with a slice trajectory. We employ the Hilbert–Huang transform (HHT) to examine these motions in detail to conduct a biomechanical analysis. The gathered data are then processed through HHT, calculating their instantaneous frequency and amplitude. The research found discernible differences between straight and slice trajectories in the golf swing’s moment of impact within the instantaneous frequency domain. An average golfer, a single handicapper, and three beginner golfers were selected as the subjects in this study and analyzed using the proposed method, respectively. For the average golfer, the head and the left leg amplitudes of the swing motions increase at the moment of impact of the swings, resulting in the slice trajectory. These results indicate that an opening of the legs and head-up movements have been detected and extracted as non-linear frequency components, reviewing the biomechanical meaning in slice trajectory motion. For the single handicapper, the hip and left arm joints could be the target joints to detect the biomechanical motion that triggered the slice trajectory. For the beginners, since their golf swing forms were not finalized, the biomechanical motions regarding slice trajectory were different from each swing, indicating that beginner golfers need more practice to fix their golf swing form first. These results revealed that our proposed framework applied to different golf levels and could help golfers to improve their golf swing skills to achieve straight trajectories.
4

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.

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Although the typological contrast between Romance and Germanic languages as verb-framed versus satellite-framed (Talmy 1985) forms the background for many empirical studies on L2 acquisition, the inconclusive picture to date calls for more differentiated, fine-grained analyses. The present study goes beyond explanations based on this typological contrast and takes into account the sources from which spatial concepts are mainly derived in order to shape the trajectory traced by the entity in motion when moving through space: the entity in V-languages versus features of the ground in S-languages. It investigates why advanced French learners of English and German have difficulty acquiring the use of spatial concepts typical of the L2s to shape the trajectory, although relevant concepts can be expressed in their L1. The analysis compares motion event descriptions, based on the same sets of video clips, of L1 speakers of the three languages to L1 French-L2 English and L1 French-L2 German speakers, showing that the learners do not fully acquire the use of L2-specific spatial concepts. We argue that encoded concepts derived from the entity in motion vs. the ground lead to a focus on different aspects of motion events, in accordance with their compatibility with these sources, and are difficult to restructure in L2 acquisition.
5

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.

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As high-throughput molecular dynamics simulations of proteins become more common and the databases housing the results become larger and more prevalent, more sophisticated methods to quickly and accurately mine large numbers of trajectories for relevant information will have to be developed. One such method, which is only recently gaining popularity in molecular biology, is the continuous wavelet transform, which is especially well-suited for time course data such as molecular dynamics simulations. We describe techniques for the calculation and analysis of wavelet transforms of molecular dynamics trajectories in detail and present examples of how these techniques can be useful in data mining. We demonstrate that wavelets are sensitive to structural rearrangements in proteins and that they can be used to quickly detect physically relevant events. Finally, as an example of the use of this approach, we show how wavelet data mining has led to a novel hypothesis related to the mechanism of the protein γδ resolvase.
6

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.

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7

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.

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8

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.

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In this paper an experimental research was performed in case of a human complex motion. The research aim was to evaluate the joint trajectories and angular variations of the main human locomotion system. Thus an experimental motion analysis was performed, by using two modern equipments in parallel, one called VICON Equipment and the other called CONTEMPLAS. The experimental activity was developed on a human subject when perform a complex motion for hitting a ball. The obtained results will be useful for the improvement of the athletes’ complex motions on sports such as football in the way of conserving the energy or to reshape the foot behavior when strikes the ball.
9

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.

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This paper shows how the instantaneous invariants for time-independent motions can be obtained from time-dependent motions. Relationships are derived between those parameters that define a time-dependent motion and the parameters that define its geometrically equivalent time-independent motion. The time-independent formulations have the advantage of being simpler than the time dependent ones, and thereby lead to more elegant and parsimonious descriptions of motions properties. The paper starts with a review of the choice of canonical coordinate systems and instantaneous invariants for time-based spherical and spatial motions. It then shows how to convert these descriptions to time-independent motions with the same geometric trajectories. New equations are given that allow the computation of the geometric invariants from time-based invariants. The paper concludes with a detailed example of the third-order motion analysis of the trajectories of an open, spatial R-R chain.
10

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.

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The calculation procedure of the trajectories of motion of the rigid rotor in the conical liquid friction bearings is offered. The equations set of motion is written for two-bearing rotor in the conical liquid friction bearings.The results are illustrated by the plots of trajectories for the conical bearings with oil lubricant.
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Статті в журналах Дисертації Книги

Дисертації з теми "Analysis of Motion Trajectories":

1

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.

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2

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.

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Cette thèse, réalisée dans le cadre d'une collaboration avec MocapLab, une entreprise spécialisée en motion capture, vise à déterminer le cadre mathématique le plus adapté et des descripteurs pertinents pour l'analyse des trajectoires de mouvement en langue des signes. En nous appuyant sur les principes du contrôle moteur, nous avons identifié le cadre défini par les formules de Frenet-Serret, incluant les paramètres de courbure, torsion et vitesse, comme particulièrement pertinent pour cette tâche. Ainsi, en introduisant de nouvelles approches d'analyse de courbes basées sur le cadre de Frenet, cette thèse contribue au développement de nouvelles méthodes dans les domaines de l'analyse de données fonctionnelles et de l'analyse de forme. La première partie de ce travail aborde le défi de l'estimation lisse des paramètres de courbures de Frenet, en traitant le problème comme une estimation de paramètres d'une équation différentielle dans SO(d), (d ≥ 1). Nous introduisons un algorithme Expectation-Maximization fonctionnel qui définit une méthode d'estimation unifiée des variables dans le groupe SE(3), fournissant des estimateurs lisses, plus fiables et robustes que les méthodes existantes. Dans la deuxième partie, deux nouvelles représentations des courbes sont introduites : les courbures de Frenet non paramétrisées et la Square Root Curvatures (SRC) transform, établissant de nouveaux cadres géométriques riemanniens pour les courbes lisses dans ℝᵈ, (d ≥ 1). En utilisant les informations géométriques d'ordre supérieur et dépendant de la paramétrisation, la Square Root Curvatures transform surpasse la représentation state-of-the-art Square-Root Velocity Function (SRVF) sur des résultats synthétiques. Étant donné une collection de courbes, ce type de géométrie nous permet de définir des critères statistiques efficaces pour estimer les formes moyennes de Karcher sur les espaces de formes riemanniens associés, qui se révèlent particulièrement performants sur des données bruitées. Enfin, ce cadre développé ouvre la voie à des applications plus pratiques dans le traitement de la langue des signes, comprenant l'étude des lois puissances sur nos données et le développement d'un modèle génératif pour le mouvement d'un point en langue des signes
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
3

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.

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4

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.

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Dans cette thèse, nous nous intéressons à la caractérisation et la reconnaissance d'activités humaines dans des vidéos. L'intérêt grandissant en vision par ordinateur pour cette thématique est motivé par une grande variété d'applications telles que l'indexation automatique de vidéos, la vidéo-surveillance, ou encore l'assistance aux personnes âgées. Dans la première partie de nos travaux, nous développons une méthode de reconnaissance d'actions élémentaires basée sur l'estimation du mouvement dans des vidéos. Les points critiques du champ vectoriel obtenu, ainsi que leurs trajectoires, sont estimés à différentes échelles spatio-temporelles. La fusion tardive de caractéristiques d'orientation de mouvement et de variation de gradient, dans le voisinage des points critiques, ainsi que la description fréquentielle des trajectoires, nous permet d'obtenir des taux de reconnaissance parmi les meilleurs de la littérature. Dans la seconde partie, nous construisons une méthode de reconnaissance d'activités en considérant ces dernières comme un enchainement temporel d'actions élémentaires. Notre méthode de reconnaissance d'actions est utilisée pour calculer la probabilité d'actions élémentaires effectuées au cours du temps. Ces séquences de probabilité évoluent sur une variété statistique appelée simplexe sémantique. Une activité est finalement représentée comme une trajectoire dans cet espace. Nous introduisons un descripteur fréquentiel de trajectoire pour classifier les différentes activités humaines en fonction de la forme des trajectoires associées. Ce descripteur prend en compte la géométrie induite par le simplexe sémantique
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
5

Almuhisen, Feda. "Leveraging formal concept analysis and pattern mining for moving object trajectory analysis." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738/document.

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Cette thèse présente un cadre de travail d'analyse de trajectoires contenant une phase de prétraitement et un processus d’extraction de trajectoires d’objets mobiles. Le cadre offre des fonctions visuelles reflétant le comportement d'évolution des motifs de trajectoires. L'originalité de l’approche est d’allier extraction de motifs fréquents, extraction de motifs émergents et analyse formelle de concepts pour analyser les trajectoires. A partir des données de trajectoires, les méthodes proposées détectent et caractérisent les comportements d'évolution des motifs. Trois contributions sont proposées : Une méthode d'analyse des trajectoires, basée sur les concepts formels fréquents, est utilisée pour détecter les différents comportements d’évolution de trajectoires dans le temps. Ces comportements sont “latents”, "emerging", "decreasing", "lost" et "jumping". Ils caractérisent la dynamique de la mobilité par rapport à l'espace urbain et le temps. Les comportements détectés sont visualisés sur des cartes générées automatiquement à différents niveaux spatio-temporels pour affiner l'analyse de la mobilité dans une zone donnée de la ville. Une deuxième méthode basée sur l'extraction de concepts formels séquentiels fréquents a également été proposée pour exploiter la direction des mouvements dans la détection de l'évolution. Enfin, une méthode de prédiction basée sur les chaînes de Markov est présentée pour prévoir le comportement d’évolution dans la future période pour une région. Ces trois méthodes sont évaluées sur ensembles de données réelles . Les résultats expérimentaux obtenus sur ces données valident la pertinence de la proposition et l'utilité des cartes produites
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
6

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.

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Cette thèse présente un cadre de travail d'analyse de trajectoires contenant une phase de prétraitement et un processus d’extraction de trajectoires d’objets mobiles. Le cadre offre des fonctions visuelles reflétant le comportement d'évolution des motifs de trajectoires. L'originalité de l’approche est d’allier extraction de motifs fréquents, extraction de motifs émergents et analyse formelle de concepts pour analyser les trajectoires. A partir des données de trajectoires, les méthodes proposées détectent et caractérisent les comportements d'évolution des motifs. Trois contributions sont proposées : Une méthode d'analyse des trajectoires, basée sur les concepts formels fréquents, est utilisée pour détecter les différents comportements d’évolution de trajectoires dans le temps. Ces comportements sont “latents”, "emerging", "decreasing", "lost" et "jumping". Ils caractérisent la dynamique de la mobilité par rapport à l'espace urbain et le temps. Les comportements détectés sont visualisés sur des cartes générées automatiquement à différents niveaux spatio-temporels pour affiner l'analyse de la mobilité dans une zone donnée de la ville. Une deuxième méthode basée sur l'extraction de concepts formels séquentiels fréquents a également été proposée pour exploiter la direction des mouvements dans la détection de l'évolution. Enfin, une méthode de prédiction basée sur les chaînes de Markov est présentée pour prévoir le comportement d’évolution dans la future période pour une région. Ces trois méthodes sont évaluées sur ensembles de données réelles . Les résultats expérimentaux obtenus sur ces données valident la pertinence de la proposition et l'utilité des cartes produites
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
7

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.

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Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This thesis presents a novel technique for clustering and classification of object trajectory based video motion clips using basis function approximation.
8

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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
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.
9

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.
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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.

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Книги з теми "Analysis of Motion Trajectories":

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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.

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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.

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Mettrick, Christopher J. Analysis of the trajectories of miniature sonobuoys. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1991.

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Workshop, on Visual Motion (1989 Irvine Calif ). Proceedings: Analysis, motion. Washington, D.C: IEEE Computer Society Press, 1989.

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Z, Bober Miroslaw. Robust motion analysis. Baldock, Hertfordshire, England: Research Studies Press, 1999.

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Aksu, Ibrahim. Performance analysis of image motion analysis algorithms. Monterey, Calif: Naval Postgraduate School, 1991.

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Canada. Defence Research Establishment Atlantic. Ship Motion Analysis Program. S.l: s.n, 1986.

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Sun, Yan. High-Orders Motion Analysis. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9191-4.

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Grossman, Robert. The analysis of control trajectories using symbolic and database computing. [Washington, DC?: National Aeronautics and Space Administration, 1991.

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Penn, Roger. Employment trajectories of Asian migrants in Rochdale: An integrated analysis. [London]: Economic and Social Research Council, 1990.

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Частини книг з теми "Analysis of Motion Trajectories":

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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AbstractIntracellular transport of organelles and biomolecules is vital for several cellular processes. Single-molecule fluorescence microscopy can illuminate molecular aspects of the dynamics of individual biomolecules that remain unresolved in ensemble experiments. For example, studying single-molecule trajectories of moving biomolecules can reveal motility properties such as velocity, diffusivity, location and duration of pauses, etc. We use single-molecule imaging to study the dynamics of microtubule-based motor proteins and their cargo in the primary cilia of living C. elegans. To this end, we employ standard fluorescent proteins, an epi-illuminated, widefield fluorescence microscope, and primarily open-source software. This chapter describes the setup we use, the preparation of samples, a protocol for single-molecule imaging in primary cilia of C. elegans, and data analysis.

Тези доповідей конференцій з теми "Analysis of Motion Trajectories":

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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.

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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.

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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.

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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.

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This study compares the piloting practices and drone flight trajectories of skilled and novice drone pilots. Markers for 3D movement analysis were attached to the fingers that move the control stick. Similarly, drones were also marked and the flight movement of the drones analyzed. These two sets of data were cross-checked to examine the characteristics of the subjects. As a result, the following results were obtained.・The expert pilot did not adjust the position of the object directly in front of the object to be photographed, but at a distance of about 90 mm in the lateral direction.・The expert moved the drone in both the first axis and the second axis directions
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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.

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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.

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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.

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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.

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Inverse dynamics analysis of human motion requires that the trajectories of the selected anatomical points are known. Therefore, standard motion capture technique by tracking marker points is generally used to obtain the trajectories. The tracking process, however, introduces high-frequency noise into the trajectories and the measured data can not be used directly to proceed in the inverse dynamic analysis. A mechanical system is consistent with kinematic data if the constraint equations of position and their time derivatives are satisfied by any parameters contained in the data set. Spurious reaction forces result from violations of the constraint equations using non consistent data. Therefore, a method is applied in this paper, whereby a new set of trajectories is generated by performing a projection of the observed positions, velocities and accelerations onto the corresponding constraint manifold to ensure the consistency of the data mentioned above. Finally, the kinematics of the system is described with the corrected data set.
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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.

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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.

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The purpose of this paper is to study the dynamic behavior of a journal bearing sleeve center by using a transient approach. Unlike other papers presenting the motion of the shaft inside the bearing sleeve, in this paper the rotor is considered rigid having only a small unbalance and the motion of the sleeve center is analyzed. The sleeve is supported by elastic elements. In addition, the axial ends of the bearing are exposed at two different pressures. To the authors’ knowledge, this paper is the first study of such a bearing configuration. Moreover, the influence of the stiffness and damping properties of the elastic element on the sleeve dynamic behavior (trajectories, motion frequencies, etc.) is also analyzed. Relative and absolute trajectories of the sleeve center and FFT analysis of the motion are presented.

Звіти організацій з теми "Analysis of Motion Trajectories":

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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.

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Nevatia, Ram. Motion Analysis and its Applications. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada232945.

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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.

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Zhou, H. Numerical analysis of slender vortex motion. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/245550.

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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.

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Rooks, Drew, and Trelanah McCalla. Human Dipping and Inserting Manipulation Motion Analysis. RPAL, December 2018. http://dx.doi.org/10.32555/2018.ir.001.

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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.

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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.

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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.

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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.

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