Готові списки джерел за темами / Automatic Motion Detection and Analysis

Добірка наукової літератури з теми "Automatic Motion Detection and Analysis"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "Automatic Motion Detection and Analysis"

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Li, Zhe, Aya Kanazuka, Atsushi Hojo, Takane Suzuki, Kazuyo Yamauchi, Shoichi Ito, Yukihiro Nomura, and Toshiya Nakaguchi. "Automatic Puncture Timing Detection for Multi-Camera Injection Motion Analysis." Applied Sciences 13, no. 12 (June 14, 2023): 7120. http://dx.doi.org/10.3390/app13127120.

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Precisely detecting puncture times has long posed a challenge in medical education. This challenge is attributable not only to the subjective nature of human evaluation but also to the insufficiency of effective detection techniques, resulting in many medical students lacking full proficiency in injection skills upon entering clinical practice. To address this issue, we propose a novel detection method that enables automatic detection of puncture times during injection without needing wearable devices. In this study, we utilized a hardware system and the YOLOv7 algorithm to detect critical features of injection motion, including puncture time and injection depth parameters. We constructed a sample of 126 medical injection training videos of medical students, and skilled observers were employed to determine accurate puncture times. Our experimental results demonstrated that the mean puncture time of medical students was 2.264 s and the mean identification error was 0.330 s. Moreover, we confirmed that there was no significant difference (p = 0.25 with a significance level of α = 0.05) between the predicted value of the system and the ground truth, which provides a basis for the validity and reliability of the system. These results show our system’s ability to automatically detect puncture times and provide a novel approach for training healthcare professionals. At the same time, it provides a key technology for the future development of injection skill assessment systems.
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Fu, Eugene Yujun, Hong Va Leong, Grace Ngai, and Stephen C. F. Chan. "Automatic fight detection in surveillance videos." International Journal of Pervasive Computing and Communications 13, no. 2 (June 5, 2017): 130–56. http://dx.doi.org/10.1108/ijpcc-02-2017-0018.

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Purpose Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner. Design/methodology/approach Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words. Findings The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach. Originality/value By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.
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DAIMON, Tatsuru, Kazuhide MOTEGI, and Hironao KAWASHIMA. "Automatic detection of driver's eye motion using video image sequence analysis." Japanese journal of ergonomics 31, no. 1 (1995): 39–50. http://dx.doi.org/10.5100/jje.31.39.

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Kotoku, Jun’ichi, Shinobu Kumagai, Ryouhei Uemura, Susumu Nakabayashi, and Takenori Kobayashi. "Automatic Anomaly Detection of Respiratory Motion Based on Singular Spectrum Analysis." International Journal of Medical Physics, Clinical Engineering and Radiation Oncology 05, no. 01 (2016): 88–95. http://dx.doi.org/10.4236/ijmpcero.2016.51009.

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Zhang, Peng Jun, Yu Cheng Bo, Hui Yuan Wang, and Qiang Li. "Fault Detection of Artillery Automatic Loading System Based on PCA." Advanced Materials Research 590 (November 2012): 459–64. http://dx.doi.org/10.4028/www.scientific.net/amr.590.459.

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The motion process of the automatic loading system is a high overloading and intermittent motion environment will bring about motor windings loosening, transmission system wear and tear, fracture, sensor failure and other security risks or system failures. In the paper no-stationary signal analysis by wavelet transform through wavelet decomposition and non-linear threshold de-noising. And use PCA established system model for on-line monitor. By calculate and analysis four kind of result to find fault source. Finally through the experimental prove the reliability of the method.
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D’Aloia, Matteo, Annalisa Longo, and Maria Rizzi. "Noisy ECG Signal Analysis for Automatic Peak Detection." Information 10, no. 2 (January 22, 2019): 35. http://dx.doi.org/10.3390/info10020035.

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Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively.
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Schütz, Anne K., Verena Schöler , E. Tobias Krause , Mareike Fischer , Thomas Müller , Conrad M. Freuling, Franz J. Conraths , Mario Stanke, Timo Homeier-Bachmann, and Hartmut H. K. Lentz. "Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes." Animals 11, no. 6 (June 9, 2021): 1723. http://dx.doi.org/10.3390/ani11061723.

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Animal activity is an indicator for its welfare and manual observation is time and cost intensive. To this end, automatic detection and monitoring of live captive animals is of major importance for assessing animal activity, and, thereby, allowing for early recognition of changes indicative for diseases and animal welfare issues. We demonstrate that machine learning methods can provide a gap-less monitoring of red foxes in an experimental lab-setting, including a classification into activity patterns. Therefore, bounding boxes are used to measure fox movements, and, thus, the activity level of the animals. We use computer vision, being a non-invasive method for the automatic monitoring of foxes. More specifically, we train the existing algorithm ‘you only look once’ version 4 (YOLOv4) to detect foxes, and the trained classifier is applied to video data of an experiment involving foxes. As we show, computer evaluation outperforms other evaluation methods. Application of automatic detection of foxes can be used for detecting different movement patterns. These, in turn, can be used for animal behavioral analysis and, thus, animal welfare monitoring. Once established for a specific animal species, such systems could be used for animal monitoring in real-time under experimental conditions, or other areas of animal husbandry.
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Hsu, Yu-Cheng, Hailiang Wang, Yang Zhao, Frank Chen, and Kwok-Leung Tsui. "Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation." Journal of Medical Internet Research 23, no. 12 (December 20, 2021): e30135. http://dx.doi.org/10.2196/30135.

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Background Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. Methods In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community’s burden of continuous health monitoring.
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Marc, O., and N. Hovius. "Amalgamation in landslide maps: effects and automatic detection." Natural Hazards and Earth System Sciences 15, no. 4 (April 2, 2015): 723–33. http://dx.doi.org/10.5194/nhess-15-723-2015.

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Abstract. Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area–frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in data sets produced by automated mapping. We present five inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area–frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and digital elevation model (DEM) analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% incorrectly amalgamated landslides missed and 3.9–4.8% correct polygons incorrectly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.
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Marc, O., and N. Hovius. "Amalgamation in landslide maps: effects and automatic detection." Natural Hazards and Earth System Sciences Discussions 2, no. 12 (December 16, 2014): 7651–78. http://dx.doi.org/10.5194/nhessd-2-7651-2014.

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Abstract. Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area-frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in datasets produced by automated mapping. We present 5 inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area-frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and DEM analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% wrongly amalgamated landslides missed and 3.9–4.8% correct polygons wrongly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.
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Дисертації з теми "Automatic Motion Detection and Analysis"

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Richards, Mark Andrew. "An intuitive motion-based input model for mobile devices." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16556/.

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Traditional methods of input on mobile devices are cumbersome and difficult to use. Devices have become smaller, while their operating systems have become more complex, to the extent that they are approaching the level of functionality found on desktop computer operating systems. The buttons and toggle-sticks currently employed by mobile devices are a relatively poor replacement for the keyboard and mouse style user interfaces used on their desktop computer counterparts. For example, when looking at a screen image on a device, we should be able to move the device to the left to indicate we wish the image to be panned in the same direction. This research investigates a new input model based on the natural hand motions and reactions of users. The model developed by this work uses the generic embedded video cameras available on almost all current-generation mobile devices to determine how the device is being moved and maps this movement to an appropriate action. Surveys using mobile devices were undertaken to determine both the appropriateness and efficacy of such a model as well as to collect the foundational data with which to build the model. Direct mappings between motions and inputs were achieved by analysing users' motions and reactions in response to different tasks. Upon the framework being completed, a proof of concept was created upon the Windows Mobile Platform. This proof of concept leverages both DirectShow and Direct3D to track objects in the video stream, maps these objects to a three-dimensional plane, and determines device movements from this data. This input model holds the promise of being a simpler and more intuitive method for users to interact with their mobile devices, and has the added advantage that no hardware additions or modifications are required the existing mobile devices.
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Richards, Mark Andrew. "An intuitive motion-based input model for mobile devices." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16556/1/Mark_Richards_Thesis.pdf.

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Traditional methods of input on mobile devices are cumbersome and difficult to use. Devices have become smaller, while their operating systems have become more complex, to the extent that they are approaching the level of functionality found on desktop computer operating systems. The buttons and toggle-sticks currently employed by mobile devices are a relatively poor replacement for the keyboard and mouse style user interfaces used on their desktop computer counterparts. For example, when looking at a screen image on a device, we should be able to move the device to the left to indicate we wish the image to be panned in the same direction. This research investigates a new input model based on the natural hand motions and reactions of users. The model developed by this work uses the generic embedded video cameras available on almost all current-generation mobile devices to determine how the device is being moved and maps this movement to an appropriate action. Surveys using mobile devices were undertaken to determine both the appropriateness and efficacy of such a model as well as to collect the foundational data with which to build the model. Direct mappings between motions and inputs were achieved by analysing users' motions and reactions in response to different tasks. Upon the framework being completed, a proof of concept was created upon the Windows Mobile Platform. This proof of concept leverages both DirectShow and Direct3D to track objects in the video stream, maps these objects to a three-dimensional plane, and determines device movements from this data. This input model holds the promise of being a simpler and more intuitive method for users to interact with their mobile devices, and has the added advantage that no hardware additions or modifications are required the existing mobile devices.
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3

Brulin, Mathieu. "Analyse sémantique d'un trafic routier dans un contexte de vidéo-surveillance." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14589/document.

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Les problématiques de sécurité, ainsi que le coût de moins en moins élevé des caméras numériques, amènent aujourd'hui à un développement rapide des systèmes de vidéosurveillance. Devant le nombre croissant de caméras et l'impossibilité de placer un opérateur humain devant chacune d'elles, il est nécessaire de mettre en oeuvre des outils d'analyse capables d'identifier des évènements spécifiques. Le travail présenté dans cette thèse s'inscrit dans le cadre d'une collaboration entre le Laboratoire Bordelais de Recherche en Informatique (LaBRI) et la société Adacis. L'objectif consiste à concevoir un système complet de vidéo-surveillance destiné à l'analyse automatique de scènes autoroutières et la détection d'incidents. Le système doit être autonome, le moins supervisé possible et doit fournir une détection en temps réel d'un évènement.Pour parvenir à cet objectif, l'approche utilisée se décompose en plusieurs étapes. Une étape d'analyse de bas-niveau, telle que l'estimation et la détection des régions en mouvement, une identification des caractéristiques d'un niveau sémantique plus élevé, telles que l'extraction des objets et la trajectoire des objets, et l'identification d'évènements ou de comportements particuliers, tel que le non respect des règles de sécurité. Les techniques employées s'appuient sur des modèles statistiques permettant de prendre en compte les incertitudes sur les mesures et observations (bruits d'acquisition, données manquantes, ...).Ainsi, la détection des régions en mouvement s'effectue au travers la modélisation de la couleur de l'arrière-plan. Le modèle statistique utilisé est un modèle de mélange de lois, permettant de caractériser la multi-modalité des valeurs prises par les pixels. L'estimation du flot optique, de la différence de gradient et la détection d'ombres et de reflets sont employées pour confirmer ou infirmer le résultat de la segmentation.L'étape de suivi repose sur un filtrage prédictif basé sur un modèle de mouvement à vitesse constante. Le cas particulier du filtrage de Kalman (filtrage tout gaussien) est employé, permettant de fournir une estimation a priori de la position des objets en se basant sur le modèle de mouvement prédéfini.L'étape d'analyse de comportement est constituée de deux approches : la première consiste à exploiter les informations obtenues dans les étapes précédentes de l'analyse. Autrement dit, il s'agit d'extraire et d'analyser chaque objet afin d'en étudier son comportement. La seconde étape consiste à détecter les évènements à travers une coupe du volume 2d+t de la vidéo. Les cartes spatio-temporelles obtenues sont utilisées pour estimer les statistiques du trafic, ainsi que pour détecter des évènements telles que l'arrêt des véhicules.Pour aider à la segmentation et au suivi des objets, un modèle de la structure de la scène et de ses caractéristiques est proposé. Ce modèle est construit à l'aide d'une étape d'apprentissage durant laquelle aucune intervention de l'utilisateur n'est requise. La construction du modèle s'effectue à travers l'analyse d'une séquence d'entraînement durant laquelle les contours de l'arrière-plan et les trajectoires typiques des véhicules sont estimés. Ces informations sont ensuite combinées pour fournit une estimation du point de fuite, les délimitations des voies de circulation et une approximation des lignes de profondeur dans l'image. En parallèle, un modèle statistique du sens de direction du trafic est proposé. La modélisation de données orientées nécessite l'utilisation de lois de distributions particulières, due à la nature périodique de la donnée. Un mélange de lois de type von-Mises est utilisée pour caractériser le sens de direction du trafic
Automatic traffic monitoring plays an important role in traffic surveillance. Video cameras are relatively inexpensive surveillance tools, but necessitate robust, efficient and automated video analysis algorithms. The loss of information caused by the formation of images under perspective projection made the automatic task of detection and tracking vehicles a very challenging problem, but essential to extract a semantic interpretation of vehicles behaviors. The work proposed in this thesis comes from a collaboration between the LaBRI (Laboratoire Bordelais de Recherche en Informatique) and the company Adacis. The aim is to elaborate a complete video-surveillance system designed for automatic incident detection.To reach this objective, traffic scene analysis proceeds from low-level processing to high-level descriptions of the traffic, which can be in a wide variety of type: vehicles entering or exiting the scene, vehicles collisions, vehicles' speed that are too fast or too low, stopped vehicles or objects obstructing part of the road... A large number of road traffic monitoring systems are based on background subtraction techniques to segment the regions of interest of the image. Resulted regions are then tracked and trajectories are used to extract a semantic interpretation of the vehicles behaviors.The motion detection is based on a statistical model of background color. The model used is a mixture model of probabilistic laws, which allows to characterize multimodal distributions for each pixel. Estimation of optical flow, a gradient difference estimation and shadow and highlight detection are used to confirm or invalidate the segmentation results.The tracking process is based on a predictive filter using a motion model with constant velocity. A simple Kalman filter is employed, which allow to predict state of objets based on a \textit{a priori} information from the motion model.The behavior analysis step contains two approaches : the first one consists in exploiting information from low-level and mid-level analysis. Objects and their trajectories are analysed and used to extract abnormal behavior. The second approach consists in analysing a spatio-temporal slice in the 3D video volume. The extracted maps are used to estimate statistics about traffic and are used to detect abnormal behavior such as stopped vehicules or wrong way drivers.In order to help the segmentaion and the tracking processes, a structure model of the scene is proposed. This model is constructed using an unsupervised learning step. During this learning step, gradient information from the background image and typical trajectories of vehicles are estimated. The results are combined to estimate the vanishing point of the scene, the lanes boundaries and a rough depth estimation is performed. In parallel, a statistical model of the trafic flow direction is proposed. To deal with periodic data, a von-Mises mixture model is used to characterize the traffic flow direction
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4

Mawla, Aya Abdul. "Real time automatic intruder detection system (RAIDS)." Thesis, University of Bristol, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319332.

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5

Hayfron-Acquah, James Ben. "Automatic gait recognition by symmetry analysis." Thesis, University of Southampton, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274080.

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6

Case, Isaac. "Automatic object detection and tracking in video /." Online version of thesis, 2010. http://hdl.handle.net/1850/12332.

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7

Aleixo, de Matos Sérgio Guilherme. "Automatic detection and analysis of cough sounds." Thesis, University of Leicester, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437913.

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Shankaranarayanan, S. "Detection of Coreferences in Automatic Specifications Analysis." Thesis, Virginia Tech, 1994. http://hdl.handle.net/10919/42360.

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Specifications on digital hardware systems typically contain descriptions and requirements expressed in natural language and diagrams of various types. The objective of the research reported here is the automatic detection of common references ("coreferences") to objects in natural language specification statements in order to permit automatic integration of requirements. This thesis describes a prototype system for detecting coreferences. First, the natural language statements are translated into conceptual graphs (semantic nets). Then, these graphs are scanned by a rule-based system to determine whether each concept that is encountered is the definition of a new concept or a reference to a previously defined concept. Tests performed on the system developed indicate a high percentage rate of correct classifications.
Master of Science
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9

Li, Yunming. "Machine vision algorithms for mining equipment automation." Thesis, Queensland University of Technology, 2000.

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10

Liu, Chang. "Human motion detection and action recognition." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1108.

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Книги з теми "Automatic Motion Detection and Analysis"

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Narasimhan, Shankar. Data reconciliation & gross error detection: An intelligent use of process data. Houston: Gulf Publishing Co., 2000.

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2

D, Lorenz Robert, and NASA Glenn Research Center, eds. Stator and rotor flux based deadbeat direct torque control of induction machines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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3

D, Lorenz Robert, and NASA Glenn Research Center, eds. Stator and rotor flux based deadbeat direct torque control of induction machines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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4

Kenny, Barbara H. Stator and rotor flux based deadbeat direct torque control of induction machines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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5

Vavrik, Ursula. A priori and a posteriori travel market segmentation: Tailoring automatic interaction detection and cluster analysis for tourism marketing. Aix-en-Provence: Centre des Hautes Etudes Touristiques, 1990.

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6

Jordache, Cornelius, and Shankar Narasimhan. Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data. Elsevier Science & Technology Books, 1999.

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7

Ph.D. (Ch.E.), Dr. Shankar Narasimhan and Ph.D. (Ch.E), Dr. Cornelius Jordache. Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data. Gulf Professional Publishing, 1999.

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8

LAND.TECHNIK AgEng 2019. VDI Verlag, 2019. http://dx.doi.org/10.51202/9783181023617.

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Dieser VDI-Bericht ist ausschließlich als PDF-Dokument erschienen! Content half of it… Analysis of Drive Trains Model-Based Chiptuning Detection of Diesel Engines 1 M. Hinrichs, P. Pickel, John Deere GmbH, Kaiserslautern R. Isermann, Institute of Automatic Control, Darmstadt An Analysis of the Energy Consumption in the High-Pressure System of an Agricultural Tractor through Modeling and Experiment 9 X. Tian, A. Vacca, Purdue University, West Lafayette, IN, USA; S. Fiorati, F. Pintore, CNH Industrial S.p.A, Modena, Italy Multi-Domain Simulation for the Assessment of the NVH Behaviour of a Tractor with Hydrostatic-Mechanical Power Split Transmission 19 G. Pasch, G. Jacobs, G. Höpfner, J. Berroth, Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Aachen Methods to evaluate steering performance of agricultural tractors 29 S. Liljenberg, M. Frederiksen, T. H. Langer, Danfoss Power Solutions, Nordborg, Denmark Tyres and Soil Soil pressure and pulling be...
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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.

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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research centers and directions in different regions of Russia; fourthly, to outline the main directions of the further development of Russian psycholinguistics. There is no doubt that in the theoretical, methodological and applied aspects, the main problems and the results of their development by Russian psycholinguistics have no analogues in world linguistics and psycholinguistics, or are represented by completely original concepts and methods. We have tried to show this uniqueness of the problematics and the methodological equipment of Russian psycholinguistics in this book. The main role in the formation of Russian psycholinguistics was played by the Moscow psycholinguistic school of A.A. Leontyev. It still defines the main directions of Russian psycholinguistics. Russian psycholinguistics (the theory of speech activity - TSA) is based on the achievements of Russian psychology: a cultural-historical approach to the analysis of mental phenomena L.S. Vygotsky and the system-activity approach of A.N. Leontyev. Moscow is the most "psycholinguistic region" of Russia - INL RAS, Moscow State University, Moscow State Linguistic University, RUDN, Moscow State Pedagogical University, Moscow State Pedagogical University, Sechenov University, Moscow State University and other Moscow universities. Saint Petersburg psycholinguists have significant achievements, especially in the study of neurolinguistic problems, ontolinguistics. The most important feature of Russian psycholinguistics is the widespread development of psycholinguistics in the regions, the emergence of recognized psycholinguistic research centers - St. Petersburg, Tver, Saratov, Perm, Ufa, Omsk, Novosibirsk, Voronezh, Yekaterinburg, Kursk, Chelyabinsk; psycholinguistics is represented in Cherepovets, Ivanovo, Volgograd, Vyatka, Kaluga, Krasnoyarsk, Irkutsk, Vladivostok, Abakan, Maikop, Barnaul, Ulan-Ude, Yakutsk, Syktyvkar, Armavir and other cities; in Belarus - Minsk, in Ukraine - Lvov, Chernivtsi, Kharkov, in the DPR - Donetsk, in Kazakhstan - Alma-Ata, Chimkent. Our researchers work in Bulgaria, Hungary, Vietnam, China, France, Switzerland. There are Russian psycholinguists in Canada, USA, Israel, Austria and a number of other countries. All scientists from these regions and countries have contributed to the development of Russian psycholinguistics, to the development of psycholinguistic theory and methods of psycholinguistic research. Their participation has not been forgotten. We tried to present the main Russian psycholinguists in the Appendix - in the sections "Scientometrics", "Monographs and Manuals" and "Dissertations", even if there is no information about them in the Electronic Library and RSCI. The principles of including scientists in the scientometric list are presented in the Appendix. Our analysis of the content of the resulting monograph on psycholinguistic research in Russia allows us to draw preliminary conclusions about some of the distinctive features of Russian psycholinguistics: 1. cultural-historical approach to the analysis of mental phenomena of L.S.Vygotsky and the system-activity approach of A.N. Leontiev as methodological basis of Russian psycholinguistics; 2. theoretical nature of psycholinguistic research as a characteristic feature of Russian psycholinguistics. Our psycholinguistics has always built a general theory of the generation and perception of speech, mental vocabulary, linked specific research with the problems of ontogenesis, the relationship between language and thinking; 3. psycholinguistic studies of speech communication as an important subject of psycholinguistics; 4. attention to the psycholinguistic analysis of the text and the development of methods for such analysis; 5. active research into the ontogenesis of linguistic ability; 6. investigation of linguistic consciousness as one of the important subjects of psycholinguistics; 7. understanding the need to create associative dictionaries of different types as the most important practical task of psycholinguistics; 8. widespread use of psycholinguistic methods for applied purposes, active development of applied psycholinguistics. The review of the main directions of development of Russian psycholinguistics, carried out in this monograph, clearly shows that the direction associated with the study of linguistic consciousness is currently being most intensively developed in modern Russian psycholinguistics. As the practice of many years of psycholinguistic research in our country shows, the subject of study of psycholinguists is precisely linguistic consciousness - this is a part of human consciousness that is responsible for generating, understanding speech and keeping language in consciousness. Associative experiments are the core of most psycholinguistic techniques and are important both theoretically and practically. The following main areas of practical application of the results of associative experiments can be outlined. 1. Education. Associative experiments are the basis for constructing Mind Maps, one of the most promising tools for systematizing knowledge, assessing the quality, volume and nature of declarative knowledge (and using special techniques and skills). Methods based on smart maps are already widely used in teaching foreign languages, fast and deep immersion in various subject areas. 2. Information search, search optimization. The results of associative experiments can significantly improve the quality of information retrieval, its efficiency, as well as adaptability for a specific person (social group). When promoting sites (promoting them in search results), an associative experiment allows you to increase and improve the quality of the audience reached. 3. Translation studies, translation automation. An associative experiment can significantly improve the quality of translation, take into account intercultural and other social characteristics of native speakers. 4. Computational linguistics and automatic word processing. The results of associative experiments make it possible to reveal the features of a person's linguistic consciousness and contribute to the development of automatic text processing systems in a wide range of applications of natural language interfaces of computer programs and robotic solutions. 5. Advertising. The use of data on associations for specific words, slogans and texts allows you to predict and improve advertising texts. 6. Social relationships. The analysis of texts using the data of associative experiments makes it possible to assess the tonality of messages (negative / positive moods, aggression and other characteristics) based on user comments on the Internet and social networks, in the press in various projections (by individuals, events, organizations, etc.) from various social angles, to diagnose the formation of extremist ideas. 7. Content control and protection of personal data. Associative experiments improve the quality of content detection and filtering by identifying associative fields in areas subject to age restrictions, personal information, tobacco and alcohol advertising, incitement to ethnic hatred, etc. 8. Gender and individual differences. The data of associative experiments can be used to compare the reactions (and, in general, other features of thinking) between men and women, different social and age groups, representatives of different regions. The directions for the further development of Russian psycholinguistics from the standpoint of the current state of psycholinguistic science in the country are seen by us, first of all:  in the development of research in various areas of linguistic consciousness, which will contribute to the development of an important concept of speech as a verbal model of non-linguistic consciousness, in which knowledge revealed by social practice and assigned by each member of society during its inculturation is consolidated for society and on its behalf;  in the expansion of the problematics, which is formed under the influence of the growing intercultural communication in the world community, which inevitably involves the speech behavior of natural and artificial bilinguals in the new object area of psycholinguistics;  in using the capabilities of national linguistic corpora in the interests of researchers studying the functioning of non-linguistic and linguistic consciousness in speech processes;  in expanding research on the semantic perception of multimodal texts, the scope of which has greatly expanded in connection with the spread of the Internet as a means of communication in the life of modern society;  in the inclusion of the problems of professional communication and professional activity in the object area of psycholinguistics in connection with the introduction of information technologies into public practice, entailing the emergence of new professions and new features of the professional ethos;  in the further development of the theory of the mental lexicon (identifying the role of different types of knowledge in its formation and functioning, the role of the word as a unit of the mental lexicon in the formation of the image of the world, as well as the role of the natural / internal metalanguage and its specificity in speech activity);  in the broad development of associative lexicography, which will meet the most diverse needs of society and cognitive sciences. The development of associative lexicography may lead to the emergence of such disciplines as associative typology, associative variantology, associative axiology;  in expanding the spheres of applied use of psycholinguistics in social sciences, sociology, semasiology, lexicography, in the study of the brain, linguodidactics, medicine, etc. This book is a kind of summarizing result of the development of Russian psycholinguistics today. Each section provides a bibliography of studies on the relevant issue. The Appendix contains the scientometrics of leading Russian psycholinguists, basic monographs, psycholinguistic textbooks and dissertations defended in psycholinguistics. The content of the publications presented here is convincing evidence of the relevance of psycholinguistic topics and the effectiveness of the development of psycholinguistic problems in Russia.
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Частини книг з теми "Automatic Motion Detection and Analysis"

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Jakobsen, Ida Marie Groth, and Maciej Plocharski. "Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis." In Image Analysis, 209–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20205-7_18.

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Tardini, Giovanni, Costantino Grana, Rossano Marchi, and Rita Cucchiara. "Shot Detection and Motion Analysis for Automatic MPEG-7 Annotation of Sports Videos." In Image Analysis and Processing – ICIAP 2005, 653–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_80.

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Alizadeh, Maryam, Melissa Cote, and Alexandra Branzan Albu. "Leaflet Free Edge Detection for the Automatic Analysis of Prosthetic Heart Valve Opening and Closing Motion Patterns from High Speed Video Recordings." In Image Analysis, 15–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59129-2_2.

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Reshadat, Vahideh, Tess Kolkman, Kalliopi Zervanou, Yingqian Zhang, Alp Akçay, Carlijn Snijder, Ryan McDonnell, et al. "Knowledge Modeling and Incident Analysis for Special Cargo." In Technologies and Applications for Big Data Value, 519–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78307-5_23.

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AbstractThe airfreight industry of shipping goods with special handling needs, also known as special cargo, suffers from nontransparent shipping processes, resulting in inefficiency. The LARA project (Lane Analysis and Route Advisor) aims at addressing these limitations and bringing innovation in special cargo route planning so as to improve operational deficiencies and customer services. In this chapter, we discuss the special cargo domain knowledge elicitation and modeling into an ontology. We also present research into cargo incidents, namely, automatic classification of incidents in free-text reports and experiments in detecting significant features associated with specific cargo incident types. Our work mainly addresses two of the main technical priority areas defined by the European Big Data Value (BDV) Strategic Research and Innovation Agenda, namely, the application of data analytics to improve data understanding and providing optimized architectures for analytics of data-at-rest and data-in-motion, the overall goal is to develop technologies contributing to the data value chain in the logistics sector. It addresses the horizontal concerns Data Analytics, Data Processing Architectures, and Data Management of the BDV Reference Model. It also addresses the vertical dimension Big Data Types and Semantics.
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Yao, Wei, and Jianwei Wu. "Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics." In Urban Informatics, 367–400. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.

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AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.
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Mitiche, Amar, and J. K. Aggarwal. "Motion Detection." In Computer Vision Analysis of Image Motion by Variational Methods, 95–142. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00711-3_4.

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Wietzke, Lennart, and Gerald Sommer. "Nonlinear Motion Detection." In Computer Analysis of Images and Patterns, 1122–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_136.

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Sueur, Jérôme. "Comparison and Automatic Detection." In Sound Analysis and Synthesis with R, 521–54. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77647-7_17.

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Bitar, Ahmad W., Jean-Philippe Ovarlez, Loong-Fah Cheong, and Ali Chehab. "Automatic Target Detection for Sparse Hyperspectral Images." In Hyperspectral Image Analysis, 435–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38617-7_15.

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Alberti, Marina, Carlo Gatta, Simone Balocco, Francesco Ciompi, Oriol Pujol, Joana Silva, Xavier Carrillo, and Petia Radeva. "Automatic Branching Detection in IVUS Sequences." In Pattern Recognition and Image Analysis, 126–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21257-4_16.

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Тези доповідей конференцій з теми "Automatic Motion Detection and Analysis"

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Fu, Eugene Yujun, Hong Va Leong, Grace Ngai, and Stephen Chan. "Automatic Fight Detection Based on Motion Analysis." In 2015 IEEE International Symposium on Multimedia (ISM). IEEE, 2015. http://dx.doi.org/10.1109/ism.2015.98.

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Jungmann, Alexander, and Bernd Kleinjohann. "Automatic feature classification for object detection based on motion analysis." In 2011 5th International Conference on Automation, Robotics and Applications (ICARA 2011). IEEE, 2011. http://dx.doi.org/10.1109/icara.2011.6144880.

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Goya, Koichiro, Xiaoxue Zhang, Kouki Kitayama, and Itaru Nagayama. "A Method for Automatic Detection of Crimes for Public Security by Using Motion Analysis." In 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2009. http://dx.doi.org/10.1109/iih-msp.2009.264.

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Shi, Zhanqun, Andrew Higson, Lin Zheng, Fengshou Gu, and Andrew Ball. "Automatic Fault Detection Using a Model-Based Approach in the Frequency Domain." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95103.

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In this paper, the model-based approach is introduced into rotation machinery fault detection to achieve an automatic feature extraction. The paper starts with a brief review of the model-based approach, including model development, residual generation and fault detection and diagnosis. The applicability of this approach to rotation machinery is then considered. In order to overcome difficulties arising from phase shift and random measurements, the statistical performance of the vibration of rotation machinery is analysed in both time and frequency domains. A consistence model is developed using stochastic process theory. After model validation, the model-based approach is implemented in AC motor fault detection. The residual is generated by comparing the new measurement and the model prediction, by both subtraction and division. Fault detection results prove that the model-based approach is applicable to fault feature extraction for rotation machinery in the frequency domain.
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Sun, Yue, Deedee Kommers, Wenjin Wang, Rohan Joshi, Caifeng Shan, Tao Tan, Ronald M. Aarts, Carola van Pul, Peter Andriessen, and Peter H. N. de With. "Automatic and Continuous Discomfort Detection for Premature Infants in a NICU Using Video-Based Motion Analysis." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857597.

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Beigi, Parmida, Septimiu E. Salcudean, Robert Rohling, and Gary C. Ng. "Automatic detection of a hand-held needle in ultrasound via phased-based analysis of the tremor motion." In SPIE Medical Imaging, edited by Robert J. Webster and Ziv R. Yaniv. SPIE, 2016. http://dx.doi.org/10.1117/12.2217073.

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Tahmoush, Dave. "An automated analysis of wide area motion imagery for moving subject detection." In SPIE Defense + Security, edited by Daniel J. Henry, Gregory J. Gosian, Davis A. Lange, Dale Linne von Berg, Thomas J. Walls, and Darrell L. Young. SPIE, 2015. http://dx.doi.org/10.1117/12.2177361.

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Shingai, Yukiya, Fusako Kusunoki, Shigenori Inagaki, and Hiroshi Mizoguchi. "Motion Detector Training with Virtual Data for Semi-Automatic Motion Analysis-Elimination of Real Training Data Collection using 3DCG Synthesis." In 2019 13th International Conference on Sensing Technology (ICST). IEEE, 2019. http://dx.doi.org/10.1109/icst46873.2019.9047711.

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Meng, Yunlong, Yong He, Jingpeng Wu, Shangbin Chen, Anan Li, and Hui Gong. "Automatic detection and quantitative analysis of cells in the mouse primary motor cortex." In Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014), edited by Qingming Luo, Lihong V. Wang, and Valery V. Tuchin. SPIE, 2014. http://dx.doi.org/10.1117/12.2068857.

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Myers, Audun, and Firas A. Khasawneh. "Dynamic State Analysis of a Driven Magnetic Pendulum Using Ordinal Partition Networks and Topological Data Analysis." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22441.

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Abstract The use of complex networks for time series analysis has recently shown to be useful as a tool for detecting dynamic state changes for a wide variety of applications. In this work, we implement the commonly used ordinal partition network to transform a time series into a network for detecting these state changes for the simple magnetic pendulum. The time series that we used are obtained experimentally from a base-excited magnetic pendulum apparatus, and numerically from the corresponding governing equations. The magnetic pendulum provides a relatively simple, non-linear example demonstrating transitions from periodic to chaotic motion with the variation of system parameters. For our method, we implement persistent homology, a shape measuring tool from Topological Data Analysis (TDA), to summarize the shape of the resulting ordinal partition networks as a tool for detecting state changes. We show that this network analysis tool provides a clear distinction between periodic and chaotic time series. Another contribution of this work is the successful application of the networks-TDA pipeline, for the first time, to signals from non-autonomous nonlinear systems. This opens the door for our approach to be used as an automatic design tool for studying the effect of design parameters on the resulting system response. Other uses of this approach include fault detection from sensor signals in a wide variety of engineering operations.
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Звіти організацій з теми "Automatic Motion Detection and Analysis"

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Robinson, David Gerald. Statistical language analysis for automatic exfiltration event detection. Office of Scientific and Technical Information (OSTI), April 2010. http://dx.doi.org/10.2172/983675.

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Butzbaugh, Joshua, Abraham SD Tidwell, and Chrissi Antonopoulos. Automatic Fault Detection & Diagnostics: Residential Market Analysis. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1670423.

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Davis, Larry, and Ross Cutler. Real-Time Periodic Motion Detection, Analysis and Application. Fort Belvoir, VA: Defense Technical Information Center, July 1999. http://dx.doi.org/10.21236/ada391942.

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Matsumoto, David, Hyisung C. Hwang, Adam M. Fullenkamp, and C. M. Laurent. Human Deception Detection from Whole Body Motion Analysis. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ada626755.

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Kong, Q. Understanding the Seismic Ground Motion Spatial Variability Using Network Analysis Community Detection. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1860919.

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Zhang, Jun. Sequential Analysis of Automatic Target Detection with Classification Algorithms and Optimality of Dynamic Decision Making Under Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, February 2013. http://dx.doi.org/10.21236/ada578207.

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Clausen, Jay, Vuong Truong, Sophia Bragdon, Susan Frankenstein, Anna Wagner, Rosa Affleck, and Christopher Williams. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45625.

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.
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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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Kulhandjian, Hovannes. AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera. Mineta Transportation Institute, November 2022. http://dx.doi.org/10.31979/mti.2022.2127.

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In 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a visual camera, infrared camera, and radar sensors combined with machine learning. The research team designed the system concept to achieve a high level of accuracy in pedestrian detection and avoidance during both the day and at night to avoid potentially fatal accidents involving pedestrians crossing a street. The working prototype of pedestrian detection and collision avoidance can be installed in present-day vehicles, with the visible camera used to detect pedestrians during the day and the infrared camera to detect pedestrians primarily during the night as well as at high glare from the sun during the day. The radar sensor is also used to detect the presence of a pedestrian and calculate their range and direction of motion relative to the vehicle. Through data fusion and deep learning, the ability to quickly analyze and classify a pedestrian’s presence at all times in a real-time monitoring system is achieved. The system can also be extended to cyclist and animal detection and avoidance, and could be deployed in an autonomous vehicle to assist in automatic braking systems (ABS).
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Deschamps, Henschel, and Robert. PR-420-123712-R01 Lateral Ground Movement Detection Capabilities Derived from Synthetic Aperture Radar. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), November 2014. http://dx.doi.org/10.55274/r0010831.

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Анотація:
The objective of this research was to quantify long-term ground deformation at the Belridge Oil Field, in the San Joaquin Valley (SJV), California using operational Interferometric Synthetic Aperture Radar (InSAR) monitoring techniques. A high spatial and temporal resolution, millimeter-precision time-series of ground deformation measurements was produced for the entire oil field from 2000 to 2012 using imagery from multiple satellites and beam modes. Trihedral Corner Reflectors (CRs) with co-located Global Navigation Satellite System (GNSS) units were used to validate the wide-area measurements along a section of Southern California Gas Company (SoCalGas) Line 7056. The GNSS measurements were also used to validate the precision of the InSAR measurements, and to determine what component of the overall motion was lateral motion. Deformation profiles over Lines 1203 were analyzed to identify periods of rapid deformation related to known pipeline incidents. Finally, we also investigated the use Multiple Aperture Interferometry (MAI) for measuring horizontal motion in the alongtrack (north-south) direction. The result is a detailed, seamless, long-term, validated time-series of ground change observations that could prove useful for further analysis of reservoir changes. Combined with injection and production data, the results may be used to extend an understanding of the geomechanics of Enhanced Oil Recovery (EOR) fields. This work reinforces the operational capability of InSAR for monitoring both EOR reservoir dynamics and deformation over buried pipelines.
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