Rozprawy doktorskie na temat „Traffic pattern recognition”
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Sprawdź 20 najlepszych rozpraw doktorskich naukowych na temat „Traffic pattern recognition”.
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Aydin, Ufuk Suat. "Traffic Sign Recognition". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610590/index.pdf.
Pełny tekst źródłas automotive technology. In the design of smarter vehicles, several research issues can be addressed
one of which is Traffic Sign Recognition (TSR). In TSR systems, the aim is to remind or warn drivers about the restrictions, dangers or other information imparted by traffic signs, beforehand. Since the existing signs are designed to draw drivers&rsquo
attention by their colors and shapes, processing of these features is one of the crucial parts in these systems. In this thesis, a Traffic Sign Recognition System, having ability of detection and identification of traffic signs even with bad visual artifacts those originate from some weather conditions or other circumstances, is developed. The developed algorithm in this thesis, segments the required color influenced by the illumination of the environment, then reconstructs the shape of partially occluded traffic sign by its remaining segments and finally, identifies it. These three stages are called as &ldquo
Segmentation&rdquo
, &ldquo
Reconstruction&rdquo
and &ldquo
Identification&rdquo
respectively, within this thesis. Due to the difficulty of analyzing partial segments to construct the main frame (a whole sign), the main complexity of the algorithm takes place in the &ldquo
Reconstruction&rdquo
stage.
Aven, Matthew. "Daily Traffic Flow Pattern Recognition by Spectral Clustering". Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1597.
Pełny tekst źródłaAli, Abdulamer T. "Computer vision aided road traffic analysis". Thesis, University of Bristol, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333953.
Pełny tekst źródłaHoughton, A. D. "The application of RAPAC to traffic monitoring". Thesis, University of Sheffield, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306208.
Pełny tekst źródłaFields, Matthew James. "Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data". [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2036.
Pełny tekst źródłaViens, Francois (Joseph Lucien Francois) Carleton University Dissertation Engineering Electrical. "A neural network approach to detect traffic anomalies in a communication network". Ottawa, 1992.
Znajdź pełny tekst źródłaVillegas, Ruben M. M. "Statistical processing for telecommunication networks applied to ATM traffic monitoring". Thesis, Loughborough University, 1997. https://dspace.lboro.ac.uk/2134/6760.
Pełny tekst źródłaCao, Meng. "Mobile and stationary computer vision based traffic surveillance techniques for advanced ITS applications". Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3350077.
Pełny tekst źródłaIncludes abstract. Title from first page of PDF file (viewed March 8, 2010). Includes bibliographical references. Issued in print and online. Available via ProQuest Digital Dissertations.
Chen, Hao. "Real-time Traffic State Prediction: Modeling and Applications". Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64292.
Pełny tekst źródłaPh. D.
Prabhakar, Yadu. "Detection and counting of Powered Two Wheelers in traffic using a single-plane Laser Scanner". Phd thesis, INSA de Rouen, 2013. http://tel.archives-ouvertes.fr/tel-00973472.
Pełny tekst źródłaKozempel, Karsten. "Entwicklung und Validierung eines Gesamtsystems zur Verkehrserfassung basierend auf Luftbildsequenzen". Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2012. http://dx.doi.org/10.18452/16487.
Pełny tekst źródłaThis dissertation should make a contribution to the further development of airborne traffic detection. The used hardware is an airborne camera system combined with an inertial measurement unit for orientation determination. Mainly computer vision algorithms are presented, which are applied afterwards the image acquisition up to the determination of the most important traffic data. After a short presentation of the used hardware the calibration of the camera''s alignment angles during test flights is explained and its accuracy is analyzed. It is shown that the orientation data doesn''t reach the specified accuracy, which is fortunately less important for traffic detection. After the image preparation, which contains the ortho image generation as well as the clipping of traffic areas, a two-stage vehicle detection algorithm is implemented, which at first rapidly creates hypotheses based on edge filters. In the second stage those hypotheses are verified by a Support Vector Machine which rejects most of the False Posititves. At good conditions the detection reaches completeness rates of up to 90 percent with a low contingent of FP detections. Subsequently a tracking algorithm based on singular value decomposition is applied to associate vehicle hypotheses in adjacent images and determine the average speed. The achieved velocities differ less than ten kph from the manually obtained data. Concluding an orientation method is presented, that automatically determines the airplane''s attitude based on GPS and image information. This is realized by extraction and matching of street segments and additional tracking of ground control points. The results have accuracies of around 0.1 to 0.2 degrees.
Taktak, Rached. "Contribution à la détection automatique des véhicules sur autoroute par vision artificielle". Vandoeuvre-les-Nancy, INPL, 1995. http://www.theses.fr/1995INPL019N.
Pełny tekst źródłaZhou, Dingfu. "Vision-based moving pedestrian recognition from imprecise and uncertain data". Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP2162/document.
Pełny tekst źródłaVision-based Advanced Driver Assistance Systems (ADAS) is a complex and challenging task in real world traffic scenarios. The ADAS aims at perceiving andunderstanding the surrounding environment of the ego-vehicle and providing necessary assistance for the drivers if facing some emergencies. In this thesis, we will only focus on detecting and recognizing moving objects because they are more dangerous than static ones. Detecting these objects, estimating their positions and recognizing their categories are significantly important for ADAS and autonomous navigation. Consequently, we propose to build a complete system for moving objects detection and recognition based on vision sensors. The proposed approach can detect any kinds of moving objects based on two adjacent frames only. The core idea is to detect the moving pixels by using the Residual Image Motion Flow (RIMF). The RIMF is defined as the residual image changes caused by moving objects with compensated camera motion. In order to robustly detect all kinds of motion and remove false positive detections, uncertainties in the ego-motion estimation and disparity computation should also be considered. The main steps of our general algorithm are the following : first, the relative camera pose is estimated by minimizing the sum of the reprojection errors of matched features and its covariance matrix is also calculated by using a first-order errors propagation strategy. Next, a motion likelihood for each pixel is obtained by propagating the uncertainties of the ego-motion and disparity to the RIMF. Finally, the motion likelihood and the depth gradient are used in a graph-cut-based approach to obtain the moving objects segmentation. At the same time, the bounding boxes of moving object are generated based on the U-disparity map. After obtaining the bounding boxes of the moving object, we want to classify the moving objects as a pedestrian or not. Compared to supervised classification algorithms (such as boosting and SVM) which require a large amount of labeled training instances, our proposed semi-supervised boosting algorithm is trained with only a few labeled instances and many unlabeled instances. Firstly labeled instances are used to estimate the probabilistic class labels of the unlabeled instances using Gaussian Mixture Models after a dimension reduction step performed via Principal Component Analysis. Then, we apply a boosting strategy on decision stumps trained using the calculated soft labeled instances. The performances of the proposed method are evaluated on several state-of-the-art classification datasets, as well as on a pedestrian detection and recognition problem.Finally, both our moving objects detection and recognition algorithms are tested on the public images dataset KITTI and the experimental results show that the proposed methods can achieve good performances in different urban scenarios
Nguyen, Tuan Anh. "Dimensioning cellular IoT network using stochastic geometry and machine learning techniques". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT014.
Pełny tekst źródłaNarrowband Internet of Things (NB-IoT) is a Low Power Wide Area technology, which was standardized in the Third Generation Partnership Project release, specifies a new random access procedure and a new transmission scheme for IoT. The advantages of the NB-IoT network are providing deep coverage, low power consumption, and support of a huge number of connections. Especially, NB-IoT can efficiently connect up to 50,000 devices per NB-IoT network cell.We focus our work on the study of NB-IoT network dimensioning. In this regard, we use stochastic geometry and machine learning techniques along with the thesis to characterize key performance indicators of the NB-IoT network, such as coverage probability, the number of required radio resource blocks, and the traffic pattern recognition and prediction based on the downlink control information. The thesis is divided into three major studies. Firstly, we derive the performance of uplink coverage probability in single-cell and multi-cell of NB-IoT network. The analytical expressions of the coverage and successful access probabilities in a single-cell NB-IoT network are presented by considering the packet arrival distribution. In the multi-cell scenario, a prediction of coverage probability is determined directly from the network parameters by using a Deep Neural Network. The subsequent analysis consists of an analytical model to calculate the required radio resource blocks in the multi-cell NB-IoT network and determine the network outage probability. This model is beneficial for operators because it clarifies how they should manage the available spectrum. Finally, the thesis addresses the recognition and prediction traffic type problems using the data collected from the Downlink Control Information. A wide group of machine learning algorithms are implemented and compared to identify the highest performances.The analysis conducted in this thesis demonstrates that stochastic geometry and machine learning techniques can serve as powerful tools to analyze the performance of the NB-IoT network. The frameworks developed in this work provide general analytical tools that can be readily extended to facilitate other research in 5G networks
Jančová, Markéta. "Generická analýza toků v počítačových sítích". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417290.
Pełny tekst źródłaLim, MJH. "Computational intelligence in e-mail traffic analysis". Thesis, 2008. https://eprints.utas.edu.au/7980/2/02Whole.pdf.
Pełny tekst źródła"Transferring a generic pedestrian detector towards specific scenes". 2012. http://library.cuhk.edu.hk/record=b5549220.
Pełny tekst źródła在本論文中,我們提出一個新的自動將通用行人檢測器適應到特定場景中的框架。這個框架分為兩個階段。在第一階段,我們探索監控錄像場景中提供的特定表征。利用這些表征,從目標場景中選擇正負樣本並重新訓練行人檢測器,該過程不斷迭代直至收斂。在第二階段,我們提出一個新的機器學習框架,該框架綜合每個樣本的標簽和比重。根據這些比重,源樣本和目標樣本被重新權重,以優化最終的分類器。這兩種方法都屬於半監督學習,僅僅需要非常少的人工干預。
使用提出的方法可以顯著提高通用行人檢測器的准確性。實驗顯示,由方法訓練出來的檢測器可以和使用大量手工標注的目標場景數據訓練出來的媲美。與其它解決類似問題的方法比較,該方法同樣好於許多已有方法。
本論文的工作已經分別於朲朱朱年和朲朱朲年在杉杅杅杅計算機視覺和模式識別會議(权杖材杒)中發表。
In recent years, significant progress has been made in learning generic pedestrian detectors from publicly available manually labeled large scale training datasets. However, when a generic pedestrian detector is applied to a specific, previously undisclosed scene where the testing data (target examples) does not match with the training data (source examples) because of variations of viewpoints, resolutions, illuminations and backgrounds, its accuracy may decrease greatly.
In this thesis, a new framework is proposed automatically adapting a pre-trained generic pedestrian detector to a specific traffic scene. The framework is two-phased. In the first phase, scene-specific cues in the video surveillance sequence are explored. Utilizing the multi-cue information, both condent positive and negative examples from the target scene are selected to re-train the detector iteratively. In the second phase, a new machine learning framework is proposed, incorporating not only example labels but also example confidences. Source and target examples are re-weighted according to their confidence, optimizing the performance of the final classifier. Both methods belong to semi-supervised learning and require very little human intervention.
The proposed approaches significantly improve the accuracy of the generic pedestrian detector. Their results are comparable with the detector trained using a large number of manually labeled frames from the target scene. Comparison with other existing approaches tackling similar problems shows that the proposed approaches outperform many contemporary methods.
The works have been published on the IEEE Conference on Computer Vision and Pattern Recognition in 2011 and 2012, respectively.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Wang, Meng.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2012.
Includes bibliographical references (leaves 42-45).
Abstracts also in Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- PedestrianDetection --- p.1
Chapter 1.1.1 --- Overview --- p.1
Chapter 1.1.2 --- StatisticalLearning --- p.1
Chapter 1.1.3 --- ObjectRepresentation --- p.2
Chapter 1.1.4 --- SupervisedStatisticalLearninginObjectDetection --- p.3
Chapter 1.2 --- PedestrianDetectioninVideoSurveillance --- p.4
Chapter 1.2.1 --- ProblemSetting --- p.4
Chapter 1.2.2 --- Challenges --- p.4
Chapter 1.2.3 --- MotivationsandContributions --- p.5
Chapter 1.3 --- RelatedWork --- p.6
Chapter 1.4 --- OrganizationsofChapters --- p.9
Chapter 2 --- Label Inferring by Multi-Cues --- p.10
Chapter 2.1 --- DataSet --- p.10
Chapter 2.2 --- Method --- p.12
Chapter 2.2.1 --- CondentPositiveExamplesofPedestrians --- p.13
Chapter 2.2.2 --- CondentNegativeExamplesfromtheBackground --- p.17
Chapter 2.2.3 --- CondentNegativeExamplesfromVehicles --- p.17
Chapter 2.2.4 --- FinalSceneSpecicPedestrianDetector --- p.19
Chapter 2.3 --- ExperimentResults --- p.20
Chapter 3 --- Transferring a Detector by Condence Propagation --- p.24
Chapter 3.1 --- Method --- p.25
Chapter 3.1.1 --- Overview --- p.25
Chapter 3.1.2 --- InitialEstimationofCondenceScores --- p.27
Chapter 3.1.3 --- Re-weightingSourceSamples --- p.27
Chapter 3.1.4 --- Condence-EncodedSVM --- p.30
Chapter 3.2 --- Experiments --- p.33
Chapter 3.2.1 --- Datasets --- p.33
Chapter 3.2.2 --- ParameterSetting --- p.35
Chapter 3.2.3 --- Results --- p.36
Chapter 4 --- Conclusions and Future Work --- p.40
Cardoso, Guilherme Jorge. "Methodologies for machine learning classification of network entities based on traffic patterns". Master's thesis, 2018. http://hdl.handle.net/10773/25131.
Pełny tekst źródłaNos últimos anos notícias sobre roubos e perdas de informação e de dados têm sido constante, levantando discussão sobre a segurança dos sistemas dos quais hoje dependemos. As comunicações são também cada vez mais privadas, pelo que os sistemas de segurança de última geração têm desenvolvido técnicas de reconhecimento de padrões para detetar e inferir a segurança sem a necessidade de processar conteúdos. Esta dissertação propõe metodologias para inferir os padrões de entidades considerando o seu tráfego de rede: se está enquadrado no comportamento de tráfego previamente conhecido, ou se a atividade gerada é incomum e, por isso, ser indicação de um possível problema. Há uma forte indicação de que o reconhecimento de padrões de comportamento continuará a liderar a investigação no domínio de soluções de segurança, não só para o tráfego de rede, mas também para outras atividades mensuráveis. Outros exemplos englobam a gestão de acesso de identidade ou programas em execução em um computador. As metodologias propõem a modelação de metadados da camada de rede OSI 3 a 5 em contagens que são posteriormente processadas por algoritmos de aprendizagem automática para classificar a atividade da rede. Esta classificação baseia-se em dois níveis: no primeiro o reconhecimento entidades ativas dentro de um domínio de rede e o segundo, se cada entidade corresponde ao padrão conhecido. As metodologias apresentadas para inferir se algo está de acordo com padrões conhecidos são transversais a outros domínios. Embora a agregação de metadados e modelação seja diferente, o processo descrito para inferir padrões é genérico o suficiente para ser aplicado a outros casos de uso, de rede ou não, ou ainda combinado em cenários mais complexos. O último capítulo inclui uma prova de conceito com dados sintéticos e algumas métricas de avaliação, para perceber se os algoritmos de classificação podem distinguir com sucesso padrões diferentes. Os testes mostraram resultados promissores, variando de 99% para classificação de entidades e 77% para 98% (dependendo da natureza da entidade) para deteção de anormalidades.
Mestrado em Engenharia de Computadores e Telemática
Dulaski, Daniel M. "Identification of a discernable centerline rumble strip pattern based on audible and haptic location recognition to improve traffic operations and safety". 2005. https://scholarworks.umass.edu/dissertations/AAI3193897.
Pełny tekst źródła"Efficient tracking of significant communication patterns in computer networks". Thesis, 2011. http://library.cuhk.edu.hk/record=b6075491.
Pełny tekst źródłaThesis (Ph.D.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (leaves 135-152).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.