Auswahl der wissenschaftlichen Literatur zum Thema „Traffic pattern recognition“
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Zeitschriftenartikel zum Thema "Traffic pattern recognition"
Zhang, Yuanqiang, und 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.
Der volle Inhalt der QuelleWu, Jian, Zhiming Cui, Victor S. Sheng, Yujie Shi und 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.
Der volle Inhalt der QuelleWANG, JING, PENGJIAN SHANG und XIAOJUN ZHAO. „A NEW TRAFFIC SPEED FORECASTING METHOD BASED ON BI-PATTERN RECOGNITION“. Fluctuation and Noise Letters 10, Nr. 01 (März 2011): 59–75. http://dx.doi.org/10.1142/s0219477511000405.
Der volle Inhalt der QuelleHong, Rongrong, Wenming Rao, Dong Zhou, Chengchuan An, Zhenbo Lu und 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.
Der volle Inhalt der QuelleHasan, Md Mehedi, und 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.
Der volle Inhalt der QuelleTettamanti, Tamás, Alfréd Csikós, Krisztián Balázs Kis, Zsolt János Viharos und István Varga. „PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK“. Transport 33, Nr. 4 (05.12.2018): 959–70. http://dx.doi.org/10.3846/16484142.2017.1352027.
Der volle Inhalt der QuelleWang, Qi, Min Lu und 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.
Der volle Inhalt der QuelleQin, Guo Feng, Yu Sun und Qi Yan Li. „Recognition of Vehicles on Geometric Morphology“. Advanced Materials Research 217-218 (März 2011): 27–32. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.27.
Der volle Inhalt der QuelleIshak, Sherif S., und 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 (Januar 1998): 56–63. http://dx.doi.org/10.3141/1634-07.
Der volle Inhalt der QuelleSohn, So Young, und Hyungwon Shin. „Pattern recognition for road traffic accident severity in Korea“. Ergonomics 44, Nr. 1 (Januar 2001): 107–17. http://dx.doi.org/10.1080/00140130120928.
Der volle Inhalt der QuelleDissertationen zum Thema "Traffic pattern recognition"
Aydin, Ufuk Suat. „Traffic Sign Recognition“. Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610590/index.pdf.
Der volle Inhalt der Quelles 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.
Der volle Inhalt der QuelleAli, Abdulamer T. „Computer vision aided road traffic analysis“. Thesis, University of Bristol, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333953.
Der volle Inhalt der QuelleHoughton, 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.
Der volle Inhalt der QuelleFields, 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.
Der volle Inhalt der QuelleViens, Francois (Joseph Lucien Francois) Carleton University Dissertation Engineering Electrical. „A neural network approach to detect traffic anomalies in a communication network“. Ottawa, 1992.
Den vollen Inhalt der Quelle findenVillegas, Ruben M. M. „Statistical processing for telecommunication networks applied to ATM traffic monitoring“. Thesis, Loughborough University, 1997. https://dspace.lboro.ac.uk/2134/6760.
Der volle Inhalt der QuelleCao, 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.
Der volle Inhalt der QuelleIncludes 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.
Der volle Inhalt der QuellePh. 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.
Der volle Inhalt der QuelleBücher zum Thema "Traffic pattern recognition"
Escalera, Sergio. Traffic-Sign Recognition Systems. London: Sergio Escalera, 2011.
Den vollen Inhalt der Quelle findenEscalera, Sergio, Xavier Baró und Oriol Pujol. Traffic-Sign Recognition Systems. Springer, 2011.
Den vollen Inhalt der Quelle findenTraffic Monitoring And Analysis 4th International Workshop Tma 2012 Vienna Austria March 12 2012 Proceedings. Springer, 2012.
Den vollen Inhalt der Quelle findenTraffic Monitoring and Analysis Lecture Notes in Computer Science Computer Communication N. Springer, 2011.
Den vollen Inhalt der Quelle findenTraffic Monitoring And Analysis First International Workshop Tma 2009 Aachen Germany May 11 2009 Proceedings. Springer, 2009.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Traffic pattern recognition"
Kerner, Boris S. „Spatiotemporal Pattern Recognition, Tracking, and Prediction“. In The Physics of Traffic, 563–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-40986-1_22.
Der volle Inhalt der QuelleFernández-Sanjurjo, Mauro, Manuel Mucientes und Víctor M. Brea. „Real-Time Traffic Monitoring with Occlusion Handling“. In Pattern Recognition and Image Analysis, 273–84. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31321-0_24.
Der volle Inhalt der QuellePramanik, Anima, Sobhan Sarkar, Chawki Djeddi und J. Maiti. „Real-Time Detection of Traffic Anomalies Near Roundabouts“. In Pattern Recognition and Artificial Intelligence, 253–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04112-9_19.
Der volle Inhalt der QuelleObagbuwa, Ibidun Christiana, und Morapedi Tshepang Duncan. „Design of an Elevator Traffic System Using MATLAB Simulation“. In Computational Intelligence in Pattern Recognition, 245–54. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3089-8_24.
Der volle Inhalt der QuelleCancela, Brais, Marcos Ortega und Manuel G. Penedo. „Path Analysis Using Directional Forces. A Practical Case: Traffic Scenes“. In 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.
Der volle Inhalt der QuelleVilariño, D. L., D. Cabello, X. M. Pardo und V. M. Brea. „Video Segmentation for Traffic Monitoring Tasks Based on Pixel-Level Snakes“. In 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.
Der volle Inhalt der QuelleGautam, Harsha, Praneet Saurabh und Ritu Prasad. „Lightweight Secure Routing Over Vehicular Ad Hoc Networks with Traffic Status“. In Computational Intelligence in Pattern Recognition, 349–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2449-3_30.
Der volle Inhalt der QuelleTang, Wenneng, Yaochen Li, Yifan Li und Bo Dong. „Efficient Point-Based Single Scale 3D Object Detection from Traffic Scenes“. In Pattern Recognition and Computer Vision, 155–67. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8432-9_13.
Der volle Inhalt der QuelleZang, Di, Yang Fang, Dehai Wang, Zhihua Wei, Keshuang Tang und Xin Li. „Long Term Traffic Flow Prediction Using Residual Net and Deconvolutional Neural Network“. In Pattern Recognition and Computer Vision, 62–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03335-4_6.
Der volle Inhalt der QuelleHillebrand, Matthias, Ulrich Kreßel, Christian Wöhler und Franz Kummert. „Traffic Sign Classifier Adaption by Semi-supervised Co-training“. In 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Traffic pattern recognition"
Wang, Sijuan, und Zhiqiang You. „Scale-variant traffic sign detection“. In Fourth International Workshop on Pattern Recognition, herausgegeben von Zhenxiang Chen, Xudong Jiang und Guojian Chen. SPIE, 2019. http://dx.doi.org/10.1117/12.2540462.
Der volle Inhalt der QuelleBuslaev, Alexander, Marina Yashina, Ruslan Abushov und Igor Kotovich. „Mathematical Problems of Pattern Recognition for Traffic“. In 2010 Seventh International Conference on Information Technology: New Generations. IEEE, 2010. http://dx.doi.org/10.1109/itng.2010.245.
Der volle Inhalt der QuelleWang, Yuan-Kai, Ching-Tang Fan und Jian-Fu Chen. „Traffic Camera Anomaly Detection“. In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.794.
Der volle Inhalt der QuelleKwan, Chiman, und Jin Zhou. „Anomaly detection in low quality traffic monitoring videos using optical flow“. In Pattern Recognition and Tracking XXIX, herausgegeben von Mohammad S. Alam. SPIE, 2018. http://dx.doi.org/10.1117/12.2303651.
Der volle Inhalt der QuelleTang, Suisui, und Lin-Lin Huang. „Traffic Sign Recognition Using Complementary Features“. In 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2013. http://dx.doi.org/10.1109/acpr.2013.63.
Der volle Inhalt der QuelleKejing Zhang und Laurie Cuthbert. „Performing traffic pattern prediction in WCDMA networks“. In 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635892.
Der volle Inhalt der QuelleFu, Meng-Yin, und Yuan-Shui Huang. „A survey of traffic sign recognition“. In 2010 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2010. http://dx.doi.org/10.1109/icwapr.2010.5576425.
Der volle Inhalt der Quelle„TRAFFIC LIGHT RECOGNITION USING CIRCULAR SEPARABILITY FILTER“. In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003741402770283.
Der volle Inhalt der QuelleQin, Fei, Bin Fang und Hengjun Zhao. „Traffic Sign Segmentation and Recognition in Scene Images“. In 2010 Chinese Conference on Pattern Recognition (CCPR). IEEE, 2010. http://dx.doi.org/10.1109/ccpr.2010.5659271.
Der volle Inhalt der QuelleSengar, Vartika, Renu Rameshan und Senthil Ponkumar. „Hierarchical Traffic Sign Recognition for Autonomous Driving“. In 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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