Literatura académica sobre el tema "PEDESTRIAN DIRECTION"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "PEDESTRIAN DIRECTION".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "PEDESTRIAN DIRECTION"
Lu, Lili, Gang Ren, Wei Wang, Chen Yu y Chenzi Ding. "Exploring the Effects of Different Walking Strategies on Bi-Directional Pedestrian Flow". Discrete Dynamics in Nature and Society 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/150513.
Texto completoZhu, Nuo, Bin Jia y Chun Fu Shao. "Pedestrian Evacuation Based on a Dynamic Parameters Model". Applied Mechanics and Materials 97-98 (septiembre de 2011): 956–59. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.956.
Texto completoZhao, Rongyong, Ping Jia, Chuanfeng Han, Yan Wang, Cuiling Li y Zhishu Zhang. "Analysis of dynamic model based on pedestrian’s abnormal posture". MATEC Web of Conferences 355 (2022): 03010. http://dx.doi.org/10.1051/matecconf/202235503010.
Texto completoHajari, Kamal Omprakash, Ujwalla Gawande y Yogesh Golhar. "Robust Pedestrian Detection and Path Prediction using Improved YOLOv5". ELCVIA Electronic Letters on Computer Vision and Image Analysis 21, n.º 2 (13 de septiembre de 2022): 40–61. http://dx.doi.org/10.5565/rev/elcvia.1538.
Texto completoWang, Weili, Jiayu Rong, Qinqin Fan, Jingjing Zhang, Xin Han y Beihua Cong. "Data-Driven Simulation of Pedestrian Movement with Artificial Neural Network". Journal of Advanced Transportation 2021 (28 de agosto de 2021): 1–16. http://dx.doi.org/10.1155/2021/5580910.
Texto completoKim, Kwangsoo, Yangho Kim y Sooyeong Kwak. "Vision Sensor Based Fuzzy System for Intelligent Vehicles". Sensors 19, n.º 4 (19 de febrero de 2019): 855. http://dx.doi.org/10.3390/s19040855.
Texto completoFelcman, Jiří y Petr Kubera. "A cellular automaton model for a pedestrian flow problem". Mathematical Modelling of Natural Phenomena 16 (2021): 11. http://dx.doi.org/10.1051/mmnp/2021002.
Texto completoHu, Xiangmin, Tao Chen, Kaifeng Deng y Guanning Wang. "Effects of the direction and speed strategies on pedestrian dynamics". Chaos: An Interdisciplinary Journal of Nonlinear Science 32, n.º 6 (junio de 2022): 063140. http://dx.doi.org/10.1063/5.0091240.
Texto completoAprilnico, Elven y Martha Leni Siregar. "Pedestrian risk analysis at Jl. Raya Citayam – Jl. Boulevard Raya Grand Depok City intersection leg using pedestrian risk index". MATEC Web of Conferences 276 (2019): 03011. http://dx.doi.org/10.1051/matecconf/201927603011.
Texto completoGuo, Ning, Rui Jiang, SC Wong, Qing-Yi Hao, Shu-Qi Xue, Yao Xiao y Chao-Yun Wu. "Experimental study on mixed traffic flow of bicycles and pedestrians". Collective Dynamics 5 (12 de agosto de 2020): A108. http://dx.doi.org/10.17815/cd.2020.108.
Texto completoTesis sobre el tema "PEDESTRIAN DIRECTION"
Shahabpoor, Erfan. "Dynamic interaction of walking humans with pedestrian structures in vertical direction experimentally based probabilistic modelling". Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/7241/.
Texto completoSchroder, Catherine Jane. "Automated creation of pedestrian route descriptions". Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/7720.
Texto completoJohansson, Anton. "Bi-directional flow in the Social Force Model". Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-43274.
Texto completoShatu, Farjana M. "Built environment impact on pedestrian route choice behaviour: Shortest vs. least directional change routes". Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/126392/1/Farjana_Shatu_Thesis.pdf.
Texto completoShimizu, Hiroaki y Tomaso Poggio. "Direction Estimation of Pedestrian from Images". 2003. http://hdl.handle.net/1721.1/30397.
Texto completoRaman, Rahul. "Pedestrian Walk Direction Estimation for Smart Surveillance". Thesis, 2019. http://ethesis.nitrkl.ac.in/10071/1/2019_PhD_RRaman_513CS1040_Pedestrian.pdf.
Texto completo趙, 光哲 y Guangzhe Zhao. "Estimation of Pedestrian Walking Direction for Driver Assistance System". Thesis, 2012. http://hdl.handle.net/2237/17275.
Texto completoLin, Chih-Chieh y 林仕杰. "Design and Implementation of YOLO Deep Learning Network Based Pedestrian Collision Avoidance and Direction Detection Technology for Intelligent Self-propelled Vehicles". Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5441090%22.&searchmode=basic.
Texto completo國立中興大學
電機工程學系所
107
In the past ten years, with the rapid development of the Internet and the maturity of hardware technology, artificial intelligence and big data analysis have become the key targets of high-tech in the future. The deep learning network can continuously learn and fix the weight of the data, and it allows the robot to perform high-complexity tasks, which are the goal of current research efforts. For intelligent self-propelled vehicles, the development of pedestrian direction detection technology is an important issue. Pedestrian direction information can avoid collision between intelligent self-propelled vehicles and crowds. By real-time object detection under the premise of big data, the deep learning can achieve higher accuracy and generalization ability more than the traditional methods. Compared with other neural networks, the YOLO based network model can obtain the effective results of object detection and object recognition in one test, and it has the advantages of high accuracy and fast operation. Besides, the YOLO model is also a one-stage classifier. In this thesis, a YOLO-PD network model is proposed by the characteristics of pedestrians. Based on the YOLOv2 algorithm and architecture improvement, this model trains a classifier that can identify six pedestrian directions, including front, left front, right front, left, right, and back. We collect several existing public pedestrian database and field samples, and define sample screening conditions to establish the pedestrian databases. The implementation process is divided into the training and testing phases. In the training phase, by using the graphics card on the personal computer, the features are extracted through the neural network, and trained by the stochastic gradient method until the loss function is converged. In the testing phase, pedestrian images were taken with a webcam with the resolution of 1920 x 1080 pixels, and the performance of YOLO-PD network model was tested on an embedded platform. The experimental results show that the YOLO-PD network model can achieve 65.52% mAP with the real-time operations by 29.80 FPS on the Xavier embedded platform. Compared with the original YOLOv1 network model, the operational speed of the proposed design is more than twice, and the mAP is also increased more than 15.86%. In addition, compared with the well-designed YOLOv2 network model, the mAP of the proposed design is slightly reduced by 0.72%, but the FPS performance can be increased by 1.45 times.
Dutta, Sankha Baran. "GPU Accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian Movement". 2014. http://hdl.handle.net/1993/23597.
Texto completoLibros sobre el tema "PEDESTRIAN DIRECTION"
Bachmann, Hugo y Walter Ammann. Vibrations in Structures. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 1987. http://dx.doi.org/10.2749/sed003e.
Texto completoA step in the right direction: Assessing the London Red Routes from a pedestrian point of view. London: Pedestrians Association, 1993.
Buscar texto completoBritain, Great. The Pelican" Pedestrian Crossings Regulations and General Directions 1987 (Statutory Instruments: 1987: 16). Stationery Office Books, 1987.
Buscar texto completoBritain, Great. The Pelican and Puffin Pedestrian Crossings General (Amendment) Directions 1998 (Statutory Instruments: 1998: 901). Stationery Office Books, 1998.
Buscar texto completoThe Zebra, Pelican and Puffin Pedestrian Crossings Regulations and General Directions (Statutory Instruments: 1997: 2400). Stationery Office Books, 1997.
Buscar texto completoComfort, Kelly y Marylaura Papalas. New Directions in Flânerie: Global Perspectives for the Twenty-First Century. Taylor & Francis Group, 2021.
Buscar texto completoNew Directions in Flânerie: Global Perspectives for the Twenty-First Century. Taylor & Francis Group, 2021.
Buscar texto completoComfort, Kelly y Marylaura Papalas. New Directions in Flânerie: Global Perspectives for the Twenty-First Century. Routledge, 2021.
Buscar texto completoComfort, Kelly y Marylaura Papalas. New Directions in Flânerie: Global Perspectives for the Twenty-First Century. Taylor & Francis Group, 2021.
Buscar texto completoCapítulos de libros sobre el tema "PEDESTRIAN DIRECTION"
Dominguez-Sanchez, Alex, Sergio Orts-Escolano y Miguel Cazorla. "Recognizing Pedestrian Direction Using Convolutional Neural Networks". En Advances in Computational Intelligence, 235–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59147-6_21.
Texto completoLv, Jiaqi, Zhenyu Na, Xin Liu, Tingting Yao y Zhian Deng. "Outlier Filtering Algorithm for Indoor Pedestrian Walking Direction Estimation". En Lecture Notes in Electrical Engineering, 2421–28. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6571-2_295.
Texto completoZhu, Wei y Harry Timmermans. "Bounded Rationality Choice Model Incorporating Attribute Threshold, Mental Effort, and Risk Attitude: Illustration to Pedestrian Walking Direction Choice Decision in Shopping Streets". En Pedestrian and Evacuation Dynamics 2008, 425–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04504-2_36.
Texto completoLu, Shunbao, Zhongliang Deng, Chen Xue, Yeqing Fang, Ruoyu Zheng y Hui Zeng. "A Pedestrian Movement Direction Recognition Method Based on Inertial Sensors". En Lecture Notes in Electrical Engineering, 781–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46632-2_67.
Texto completoHu, Zhichao, Gabrielle Halberg, Carolynn R. Jimenez y Marilyn A. Walker. "Entrainment in Pedestrian Direction Giving: How Many Kinds of Entrainment?" En Signals and Communication Technology, 151–64. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21834-2_14.
Texto completoChraibi, Mohcine, Martina Freialdenhoven, Andreas Schadschneider y Armin Seyfried. "Modeling the Desired Direction in a Force-Based Model for Pedestrian Dynamics". En Traffic and Granular Flow '11, 263–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39669-4_25.
Texto completoSantoshi, G. y S. R. Mishra. "Pedestrian with Direction Detection Using the Combination of Decision Tree Learning and SVM". En Advances in Intelligent Systems and Computing, 249–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13728-5_28.
Texto completoTubino, Federica. "Probabilistic Analysis of Human-Structure Interaction in the Vertical Direction for Pedestrian Bridges". En Conference Proceedings of the Society for Experimental Mechanics Series, 117–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54777-0_15.
Texto completoTachikawa, Yuji. "Conclusions and Further Directions". En N=2 Supersymmetric Dynamics for Pedestrians, 201–5. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08822-8_13.
Texto completoEledeisy, Mohamed. "Inclusive Neighborhoods in a Healthy City: Walkability Assessment and Guidance in Rome". En The Urban Book Series, 959–67. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29515-7_85.
Texto completoActas de conferencias sobre el tema "PEDESTRIAN DIRECTION"
Larabi, S. y A. Bensebaa. "Pedestrian walking direction from video". En 8th International Conference on Imaging for Crime Detection and Prevention (ICDP 2017). Institution of Engineering and Technology, 2017. http://dx.doi.org/10.1049/ic.2017.0045.
Texto completoLarabi, Slimane y Amina Bensebaa. "Estimation of pedestrian walking direction from video". En 2017 International Conference on Mathematics and Information Technology (ICMIT). IEEE, 2017. http://dx.doi.org/10.1109/mathit.2017.8259689.
Texto completoAyub, Shahid, Behzad Momahed Heravi, Alireza Bahraminasab y Bahram Honary. "Pedestrian Direction of Movement Determination Using Smartphone". En 2012 6th International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST). IEEE, 2012. http://dx.doi.org/10.1109/ngmast.2012.36.
Texto completoMao, Lina y Linyan Tang. "Pedestrian Detection Based on Gradient Direction Histogram". En 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2022. http://dx.doi.org/10.1109/ipec54454.2022.9777626.
Texto completoHe, Bate y Eisuke Kita. "Pedestrian Walking Direction Prediction Using Generative Adversarial Networks". En 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020. http://dx.doi.org/10.1109/smc42975.2020.9283115.
Texto completoS, Sukesh Babu V. y Rahul Raman. "Pedestrian Direction Estimation: An Approach via Perspective Distortion Patterns". En 2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT). IEEE, 2023. http://dx.doi.org/10.1109/icitiit57246.2023.10068588.
Texto completoHoshi, Hisashi, Masahiro Fujii, Atsushi Ito, Yu Watanabe y Hiroyuki Hatano. "A Study on Direction Estimation for Pedestrian Dead Reckoning". En 2014 Second International Symposium on Computing and Networking (CANDAR). IEEE, 2014. http://dx.doi.org/10.1109/candar.2014.68.
Texto completoManos, Adi, Itzik Klein y Tamir Hazan. "Gravity Direction Estimation and Heading Determination for Pedestrian Navigation". En 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2018. http://dx.doi.org/10.1109/ipin.2018.8533689.
Texto completoItoh, Makoto, Toshiyuki Inagaki y Hiroto Tanaka. "Haptic steering direction guidance for pedestrian-vehicle collision avoidance". En 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2012. http://dx.doi.org/10.1109/icsmc.2012.6378305.
Texto completoKolcu, Cihangir y Bulent Bolat. "Yayaların yürüyüş rotalarının belirlenmesi detection of walking direction for pedestrian". En 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT). IEEE, 2018. http://dx.doi.org/10.1109/ebbt.2018.8391426.
Texto completoInformes sobre el tema "PEDESTRIAN DIRECTION"
Simizu, Hiroaki y Tomaso Poggio. Direction Estimation of Pedestrian from Images. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2003. http://dx.doi.org/10.21236/ada459729.
Texto completoKulhandjian, Hovannes. AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera. Mineta Transportation Institute, noviembre de 2022. http://dx.doi.org/10.31979/mti.2022.2127.
Texto completoMartinez, Kimberly D. y Gaojian Huang. Exploring the Effects of Meaningful Tactile Display on Perception and Preference in Automated Vehicles. Mineta Transportation Institute, octubre de 2022. http://dx.doi.org/10.31979/mti.2022.2164.
Texto completo