Auswahl der wissenschaftlichen Literatur zum Thema „Traffic accidents in motion pictures“
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Zeitschriftenartikel zum Thema "Traffic accidents in motion pictures"
Dwiatmoko, Hermanto, Muhammad Isradi, Joewono Prasetijo, Munzilah MD Rohani, Syafiadi Rizki Abdila und Andri Irfan Rifai. „Accident Prevention and Traffic Safety for Sekolah Alam Robbani Bekasi - (KidSafe)“. Communautaire: Journal of Community Service 2, Nr. 1 (25.06.2023): 56–63. http://dx.doi.org/10.61987/communautaire.v2i1.162.
Der volle Inhalt der QuellePelenyte-Vyšniauskiene, Lina, und Algirdas Jurkauskas. „RESEARCH INTO OCCUPANT'S MOTION IN VEHICLES DURING CRASHES“. TRANSPORT 19, Nr. 4 (31.08.2004): 184–90. http://dx.doi.org/10.3846/16484142.2004.9637974.
Der volle Inhalt der QuellePrentkovskis, Olegas, Edgar Sokolovskij und Vilius Bartulis. „INVESTIGATING TRAFFIC ACCIDENTS: A COLLISION OF TWO MOTOR VEHICLES“. TRANSPORT 25, Nr. 2 (30.06.2010): 105–15. http://dx.doi.org/10.3846/transport.2010.14.
Der volle Inhalt der QuelleHitendra Wamborikar, Priyank Bhatt, Swapnil Date, Naresh Dhaniwala und Khizar Khan. „The multi-modality approach in a case of fracture of the humerus with an ipsilateral floating knee: A case report“. International Journal of Research in Pharmaceutical Sciences 11, SPL4 (21.12.2020): 1088–92. http://dx.doi.org/10.26452/ijrps.v11ispl4.4248.
Der volle Inhalt der QuelleKurczyński, Dariusz, und Andrzej Zuska. „Analysis of the Impact of Invisible Road Icing on Selected Parameters of a Minibus Vehicle“. Sensors 22, Nr. 24 (12.12.2022): 9726. http://dx.doi.org/10.3390/s22249726.
Der volle Inhalt der QuelleWang, Feng, und Zhaofeng Zhang. „Route Control and Behavior Decision of Intelligent Driverless Truck Based on Artificial Intelligence Technology“. Wireless Communications and Mobile Computing 2022 (07.09.2022): 1–10. http://dx.doi.org/10.1155/2022/7025081.
Der volle Inhalt der QuelleBresch, M., J. Shi und R. Kokozinski. „Employing beam-forming for estimating the direction of arrival in a multi-path propagation environment“. Advances in Radio Science 3 (12.05.2005): 151–55. http://dx.doi.org/10.5194/ars-3-151-2005.
Der volle Inhalt der QuelleKrasavin, P. A., und V. S. Nadezhdin. „The influence of the set angle of steered wheels on safety of vehicle in curvilinear motion“. Izvestiya MGTU MAMI 8, Nr. 1-1 (10.01.2014): 23–25. http://dx.doi.org/10.17816/2074-0530-67674.
Der volle Inhalt der QuelleMicu, Elena Alexandra, Eugene John Obrien, Abdollah Malekjafarian und Michael Quilligan. „Estimation of Extreme Load Effects on Long-Span Bridges Using Traffic Image Data“. Baltic Journal of Road and Bridge Engineering 13, Nr. 4 (21.12.2018): 429–46. http://dx.doi.org/10.7250/bjrbe.2018-13.427.
Der volle Inhalt der QuellePrasolenko, O., D. Burko, T. Lutsenko und I. Tkachenko. „PATTERNS OF DRIVERS' ATTENTION CHANGES WHEN MOTION VIA URBAN STREETS IN THE EVENING TWILIGHT CONDITIONS“. Municipal economy of cities 3, Nr. 156 (01.07.2020): 41–45. http://dx.doi.org/10.33042/2522-1809-2020-3-156-41-45.
Der volle Inhalt der QuelleDissertationen zum Thema "Traffic accidents in motion pictures"
Zhou, Dingfu. „Vision-based moving pedestrian recognition from imprecise and uncertain data“. Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP2162/document.
Der volle Inhalt der QuelleVision-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
Bücher zum Thema "Traffic accidents in motion pictures"
Jordán, Guillermo Arriaga. 21 grams. London: Faber and Faber, 2003.
Den vollen Inhalt der Quelle findenHans-Peter, Wipplinger, Chaubal Anjalie 1969- und Museum Moderner Kunst Passau, Hrsg. Dieter Buchhart: Cut. Wien: Schlebrügge, 2007.
Den vollen Inhalt der Quelle findenHaggis, Paul. Crash: Story. 2. Aufl. [S.l: s.n., 2003.
Den vollen Inhalt der Quelle findenGoodbye, goodness: A novel. New York: Open City Books, 2005.
Den vollen Inhalt der Quelle findenAcland, Charles R. Screen traffic: Movies, multiplexes, and global culture. Durham: Duke University Press, 2003.
Den vollen Inhalt der Quelle findenSherrill, Martha. My last movie star: A novel of Hollywood. New York: Random House, 2003.
Den vollen Inhalt der Quelle findenPicturing American modernity: Traffic, technology, and the silent cinema. Durham: Duke University Press, 2008.
Den vollen Inhalt der Quelle findenSchwartz, John Burnham. Reservation Road. Thorndike, Me: Thorndike Press, 1999.
Den vollen Inhalt der Quelle findenSchwartz, John Burnham. Reservation road. London: Orion, 1999.
Den vollen Inhalt der Quelle findenSchwartz, John Burnham. Reservation road. London: QPD, 1998.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Traffic accidents in motion pictures"
Goodwin, Brian D., Sajal Chirvi und Frank A. Pintar. „Injury Mechanisms in Traffic Accidents“. In Handbook of Human Motion, 2363–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-14418-4_93.
Der volle Inhalt der QuelleMattos, Garrett A. „Vehicle Occupants in Traffic Accidents“. In Handbook of Human Motion, 2399–416. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-14418-4_94.
Der volle Inhalt der QuelleGoodwin, Brian D., Sajal Chirvi und Frank A. Pintar. „Injury Mechanisms in Traffic Accidents“. In Handbook of Human Motion, 1–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-30808-1_93-1.
Der volle Inhalt der QuelleMattos, Garrett A. „Vehicle Occupants in Traffic Accidents“. In Handbook of Human Motion, 1–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-30808-1_94-1.
Der volle Inhalt der QuelleElmi, Zahra, und Soheila Elmi. „Autonomous Vehicle Path Planning Using MPC and APF“. In Motion Planning. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.99628.
Der volle Inhalt der QuelleVexler, Julian, und Stefan Kramer. „Classifying Aircraft Categories from Magnetometry Data Using a Hypotheses-Based Multi-Task Framework“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230647.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Traffic accidents in motion pictures"
Akoz, O., und M. E. Karsligil. „Severity detection of traffic accidents at intersections based on vehicle motion analysis and multiphase linear regression“. In 2010 13th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2010). IEEE, 2010. http://dx.doi.org/10.1109/itsc.2010.5624990.
Der volle Inhalt der QuelleTakao, Kisa, Hironori Uchida, Yujie Li und Yoshihisa Nakatoh. „The Effect of Auditory Stimulus on Gazing Behavior in Driving Assistance Systems“. In 10th International Conference on Human Interaction and Emerging Technologies (IHIET 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004058.
Der volle Inhalt der QuelleBalci, İbrahim, und Ahmet Kirli. „Development of a Multibody Dynamic Model of a Seated Occupant to Evaluate More Realistic ISO-Standard Motion Sickness Dose Value“. In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0853.
Der volle Inhalt der QuelleWei, Zehong, Jiaxin Wang, Lei Shi, Qingju Wang, Yuanyuan Ren und Xuelian Zheng. „Driving Fatigue Recognition Based on the Combination of Multimodal Features“. In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005235.
Der volle Inhalt der QuelleMatsumura, Hideki, Motoki Sugiyama und Takekazu IWATA. „Study on a Method for Reconstructing Pre-Crash Situations Using Data of an Event Data Recorder and a Dashboard Camera“. In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2891.
Der volle Inhalt der QuelleBukaty, Michael E. „A Vehicle Identification System For Surveillance Applications“. In Machine Vision. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/mv.1987.fa5.
Der volle Inhalt der QuelleKoppisetty, D. V. Suresh, S. S. Akhil Hawaldar und Hamid M. Lankarani. „Comparison of Dummy and Human Body Models in Automotive Side Impact Collisions According to the Regulatory Standards“. In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10680.
Der volle Inhalt der QuelleMaull, Thomas, Lauren Eichaker, Sean Buczek und Troy Graham. „Rolling Resistance of a Skateboard“. In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23910.
Der volle Inhalt der QuelleSemones, Paul T., und David A. Renfroe. „Magnitude of Axle Tramp Response to Partially Detreaded Tire Imbalance in Highway-Speed Driving“. In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70287.
Der volle Inhalt der QuelleRooseleer, Frédéric, Barry Kirwan, Elizabeth Humm und Diana Paola Moreno Alarcon. „'The Application of Human Factors in Wake Vortex Encounter Flight Simulations for the Reduction of Flight Upset Risk and Startle Response“. In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001565.
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