Gotowa bibliografia na temat „Traffic pattern recognition”
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Artykuły w czasopismach na temat "Traffic pattern recognition"
Zhang, Yuanqiang, i Weifeng Li. "Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data". Sensors 22, nr 16 (22.08.2022): 6307. http://dx.doi.org/10.3390/s22166307.
Pełny tekst źródłaWu, Jian, Zhiming Cui, Victor S. Sheng, Yujie Shi i Pengpeng Zhao. "Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition". Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/834013.
Pełny tekst źródłaWANG, JING, PENGJIAN SHANG i XIAOJUN ZHAO. "A NEW TRAFFIC SPEED FORECASTING METHOD BASED ON BI-PATTERN RECOGNITION". Fluctuation and Noise Letters 10, nr 01 (marzec 2011): 59–75. http://dx.doi.org/10.1142/s0219477511000405.
Pełny tekst źródłaHong, Rongrong, Wenming Rao, Dong Zhou, Chengchuan An, Zhenbo Lu i Jingxin Xia. "Commuting Pattern Recognition Using a Systematic Cluster Framework". Sustainability 12, nr 5 (27.02.2020): 1764. http://dx.doi.org/10.3390/su12051764.
Pełny tekst źródłaHasan, Md Mehedi, i Jun-Seok Oh. "GIS-Based Multivariate Spatial Clustering for Traffic Pattern Recognition using Continuous Counting Data". Transportation Research Record: Journal of the Transportation Research Board 2674, nr 10 (24.07.2020): 583–98. http://dx.doi.org/10.1177/0361198120937019.
Pełny tekst źródłaTettamanti, Tamás, Alfréd Csikós, Krisztián Balázs Kis, Zsolt János Viharos i István Varga. "PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK". Transport 33, nr 4 (5.12.2018): 959–70. http://dx.doi.org/10.3846/16484142.2017.1352027.
Pełny tekst źródłaWang, Qi, Min Lu i Qingquan Li. "Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories". Sensors 20, nr 4 (17.02.2020): 1084. http://dx.doi.org/10.3390/s20041084.
Pełny tekst źródłaQin, Guo Feng, Yu Sun i Qi Yan Li. "Recognition of Vehicles on Geometric Morphology". Advanced Materials Research 217-218 (marzec 2011): 27–32. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.27.
Pełny tekst źródłaIshak, Sherif S., i Haitham M. Al-Deek. "Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents". Transportation Research Record: Journal of the Transportation Research Board 1634, nr 1 (styczeń 1998): 56–63. http://dx.doi.org/10.3141/1634-07.
Pełny tekst źródłaSohn, So Young, i Hyungwon Shin. "Pattern recognition for road traffic accident severity in Korea". Ergonomics 44, nr 1 (styczeń 2001): 107–17. http://dx.doi.org/10.1080/00140130120928.
Pełny tekst źródłaRozprawy doktorskie na temat "Traffic pattern recognition"
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 źródłaKsiążki na temat "Traffic pattern recognition"
Xavier, Baró, Pujol Oriol, Vitrià Jordi, Radeva Petia i SpringerLink (Online service), red. Traffic-Sign Recognition Systems. London: Sergio Escalera, 2011.
Znajdź pełny tekst źródłaEscalera, Sergio, Xavier Baró i Oriol Pujol. Traffic-Sign Recognition Systems. Springer, 2011.
Znajdź pełny tekst źródłaTraffic Monitoring And Analysis 4th International Workshop Tma 2012 Vienna Austria March 12 2012 Proceedings. Springer, 2012.
Znajdź pełny tekst źródłaTraffic Monitoring and Analysis Lecture Notes in Computer Science Computer Communication N. Springer, 2011.
Znajdź pełny tekst źródłaTraffic Monitoring And Analysis First International Workshop Tma 2009 Aachen Germany May 11 2009 Proceedings. Springer, 2009.
Znajdź pełny tekst źródłaCzęści książek na temat "Traffic pattern recognition"
Kerner, Boris S. "Spatiotemporal Pattern Recognition, Tracking, and Prediction". W The Physics of Traffic, 563–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-40986-1_22.
Pełny tekst źródłaFernández-Sanjurjo, Mauro, Manuel Mucientes i Víctor M. Brea. "Real-Time Traffic Monitoring with Occlusion Handling". W Pattern Recognition and Image Analysis, 273–84. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31321-0_24.
Pełny tekst źródłaPramanik, Anima, Sobhan Sarkar, Chawki Djeddi i J. Maiti. "Real-Time Detection of Traffic Anomalies Near Roundabouts". W Pattern Recognition and Artificial Intelligence, 253–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04112-9_19.
Pełny tekst źródłaObagbuwa, Ibidun Christiana, i Morapedi Tshepang Duncan. "Design of an Elevator Traffic System Using MATLAB Simulation". W Computational Intelligence in Pattern Recognition, 245–54. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3089-8_24.
Pełny tekst źródłaCancela, Brais, Marcos Ortega i Manuel G. Penedo. "Path Analysis Using Directional Forces. A Practical Case: Traffic Scenes". W Pattern Recognition and Image Analysis, 366–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_43.
Pełny tekst źródłaVilariño, D. L., D. Cabello, X. M. Pardo i V. M. Brea. "Video Segmentation for Traffic Monitoring Tasks Based on Pixel-Level Snakes". W Pattern Recognition and Image Analysis, 1074–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_124.
Pełny tekst źródłaGautam, Harsha, Praneet Saurabh i Ritu Prasad. "Lightweight Secure Routing Over Vehicular Ad Hoc Networks with Traffic Status". W Computational Intelligence in Pattern Recognition, 349–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2449-3_30.
Pełny tekst źródłaTang, Wenneng, Yaochen Li, Yifan Li i Bo Dong. "Efficient Point-Based Single Scale 3D Object Detection from Traffic Scenes". W Pattern Recognition and Computer Vision, 155–67. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8432-9_13.
Pełny tekst źródłaZang, Di, Yang Fang, Dehai Wang, Zhihua Wei, Keshuang Tang i Xin Li. "Long Term Traffic Flow Prediction Using Residual Net and Deconvolutional Neural Network". W Pattern Recognition and Computer Vision, 62–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03335-4_6.
Pełny tekst źródłaHillebrand, Matthias, Ulrich Kreßel, Christian Wöhler i Franz Kummert. "Traffic Sign Classifier Adaption by Semi-supervised Co-training". W Artificial Neural Networks in Pattern Recognition, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33212-8_18.
Pełny tekst źródłaStreszczenia konferencji na temat "Traffic pattern recognition"
Wang, Sijuan, i Zhiqiang You. "Scale-variant traffic sign detection". W Fourth International Workshop on Pattern Recognition, redaktorzy Zhenxiang Chen, Xudong Jiang i Guojian Chen. SPIE, 2019. http://dx.doi.org/10.1117/12.2540462.
Pełny tekst źródłaBuslaev, Alexander, Marina Yashina, Ruslan Abushov i Igor Kotovich. "Mathematical Problems of Pattern Recognition for Traffic". W 2010 Seventh International Conference on Information Technology: New Generations. IEEE, 2010. http://dx.doi.org/10.1109/itng.2010.245.
Pełny tekst źródłaWang, Yuan-Kai, Ching-Tang Fan i Jian-Fu Chen. "Traffic Camera Anomaly Detection". W 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.794.
Pełny tekst źródłaKwan, Chiman, i Jin Zhou. "Anomaly detection in low quality traffic monitoring videos using optical flow". W Pattern Recognition and Tracking XXIX, redaktor Mohammad S. Alam. SPIE, 2018. http://dx.doi.org/10.1117/12.2303651.
Pełny tekst źródłaTang, Suisui, i Lin-Lin Huang. "Traffic Sign Recognition Using Complementary Features". W 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2013. http://dx.doi.org/10.1109/acpr.2013.63.
Pełny tekst źródłaKejing Zhang i Laurie Cuthbert. "Performing traffic pattern prediction in WCDMA networks". W 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635892.
Pełny tekst źródłaFu, Meng-Yin, i Yuan-Shui Huang. "A survey of traffic sign recognition". W 2010 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2010. http://dx.doi.org/10.1109/icwapr.2010.5576425.
Pełny tekst źródła"TRAFFIC LIGHT RECOGNITION USING CIRCULAR SEPARABILITY FILTER". W International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003741402770283.
Pełny tekst źródłaQin, Fei, Bin Fang i Hengjun Zhao. "Traffic Sign Segmentation and Recognition in Scene Images". W 2010 Chinese Conference on Pattern Recognition (CCPR). IEEE, 2010. http://dx.doi.org/10.1109/ccpr.2010.5659271.
Pełny tekst źródłaSengar, Vartika, Renu Rameshan i Senthil Ponkumar. "Hierarchical Traffic Sign Recognition for Autonomous Driving". W 9th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008924703080320.
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