فهرست منابع
Afsar, P. Cortez, P. Santos, H. 2022. “Automatic Visual Detection of Human Behavior: A Review from 2000 to 2014.” Expert. Syst. Appl., 42: 6935–6956.
Alam, A. Jaffery, Z.A. and H. Sharma, 2022. “A Cost-Effective Computer Vision-Based Vehicle Detection System. Concurr”. Eng (30): 148–158.
Ali, A.T. Dagless, E.L. 1990. “Vehicle and Pedestrian Detection and Tracking”. In Proceedings of the IEE Colloquium on Image Analysis for Transport Applications; IET.
Al-Shemarry, M.S.and Y Li. 2020. “Developing Learning-Based Preprocessing Methods for Detecting Complicated Vehicle Licence Plates”. IEEE Access, 8: 170951–170966.
Arif, M.U. Farooq, M.U. Raza, R.H. 2020. “A Comprehensive Review of Vehicle Detection Techniques under Shadow Conditions Using Computer Vision and Deep Learning”. IEEE Access, 10: 104863-104886.
Batura, O. Regeczi, D. Vassilev, A. Yagafarova, A. Bani, E. Bonneau, V. 2021. Artificial Intelligence in Road Transport: Annex to Cost of Non-Europe Report.
Changzhen, X. Cong, W. Weixin, M. Yanmei, S. 2016. “A Traffic Sign Detection Algorithm Based on Deep Convolutional Neural Network”. IEEE International Conference on Signal and Image Processing, 676–679.
Chen, W. Wang, W. Wang, K. 2020. “Lane Departure Warning Systems and Lane Line Detection Methods Based on Image Processing and Semantic Segmentation: Traffic Transport’. Journal of Traffic and Transportation. Engineering (English Edition).
Darapaneni, N. Mogeraya, K. Mandal, S. Narayanan, A. Siva, P. 2020. “Computer Vision Based License Plate Detection for Automated Vehicle Parking Management System”. In Proceedings of the 2020 11th IEEE Annual Ubiquitous Computing & Mobile Communication Conf., 800–805.
Desai, G.G. Bartakke, P.P. 2018. “Real-Time Implementation of Indian License Plate Recognition System”. In Proceedings of the International Conference on Computer, 1–5.
Dewangan, D.K. Sahu, S.P. 2021. Road Detection Using Semantic Segmentation-Based Convolutional Neural Network for Intelligent Vehicle System. Data Engineering and Communication Technology: 629–637.
Dilek, E., Dener, M.2023. “Computer Vision Applications in Intelligent Transportation Systems: A SurveyNetwork”. In Sensors.
Dollar, P. Wojek, C. Schiele, B. Perona, P. 2011. “Pedestrian Detection: An Evaluation of the State of the Art”. IEEE Trans. Pattern Anal. Mach. Intell., 34: 743–761.
Du, X. Ang, M.H. Rus, D. 2017. “Car Detection for Autonomous Vehicle: LIDAR and Vision Fusion Approach through Deep Learning Framework”. In Proceedings of the 2017 IEEE/ RSJ International Conference on Intelligent Robots and Systems (IROS): 749–754.
Enzweiler, M. Gavrila, D.M. 2008. “Monocular Pedestrian Detection: Survey and Experiments”. IEEE Trans. Pattern Anal. Mach. Intell., 31: 2179–2195.
Farhat, A.A.H. Al-Zawqari, A.; Hommos, O. 2017, "OCR-Based Hardware Implementation for Qatari Number Plate on the Zynq SoC," 2017 9th IEEE-GCC Conference and Exhibition (GCCCE), Manama, Bahrain, 1-9.
Fukui, H. Yamashita, T. Yamauchi, Y. Fujiyoshi, H. Murase, H. 2015. “Pedestrian Detection Based on Deep Convolutional Neural Network with Ensemble Inference Network”. In Proceedings of the 2015 IEEE Intelligent Vehicles Symposium, 223–228.
Gopalan, R. Hong, T. Shneier, M. Chellappa, R. 2012. “A Learning Approach towards Detection and Tracking of Lane Markings”. IEEE Trans. 13: 1088–1098.
Greenhalgh, J., M. Mirmehdi. 2012. “Real-Time Detection and Recognition of Road Traffic Signs”. IEEE Trans. Intell. Transp. Syst. 13: 1498–1506.
Hashmi, S.N. Kumar, K. Khandelwal, S. Lochan, D. Mittal, S. 2019. “Real Time License Plate Recognition from Video Streams Using Deep Learning”. Int. Conference on Deep, 20-33.
Hommos, O. Al-Qahtani, A. Farhat, A. Al-Zawqari, A. 2016. “HD Qatari ANPR System”. In Proceedings of the 2016 International Conference on Industrial Informatics and Computer Systems (CIICS), 1–5.
Hu, F. Tian, Z. and, Y Li. 2018. “A Combined Clustering and Image Mapping Based Point Cloud Segmentation for 3D Object Detection”. Chinese Control and Decision Conference.
Huang, S. He, Y. Chen, X. 2021. “M-YOLO: A Nighttime Vehicle Detection Method Combining Mobilenet v2 and YOLO V3”. In Proceedings of the Journal of Physics: Conference Series; IOP Publishing: Bristol, UK, Volume 1883.
Hurtado-Gómez, J. Romo, J.D. Salazar-Cabrera, R. Pachon de la Cruz, A. and, J.M Madrid Molina. 2021. Traffic Signal Control System Based on Intelligent Transportation System and Reinforcement Learning. Electronics, 10: 2363.
Huval, B. Wang, T. Tandon, S. Kiske, J. Song, W. Pazhayampallil, R. 2015. An Empirical Evaluation of Deep Learning on Highway Driving. Arxiv.
Jiao, J. Wang, H. 2022. “Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach”. Transp. Res. Rec. 2676: 55–65.
John, V. Mita, S. Liu, Z. Qi, B. 2015. “Pedestrian Detection in Thermal Images Using Adaptive Fuzzy C-Means Clustering and Convolutional Neural Networks”. In Proceedings of the 2015 14th IAPR International Conference on Machine Vision Applications.
Joshi, G. Kaul, S. Singh, A. 2021. “Automated Vehicle Numberplate Detection and Recognition.” In Proceedings of the 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 465–469.
Jung, S. Lee, U. Jung, J. Shim, D.H. 2016. “Real-Time Traffic Sign Recognition System with Deep Convolutional Neural Network”. In Proceedings of the 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, 31–34.
Kim, J. Lee, M. 2014. “Robust Lane Detection Based on Convolutional Neural Network and Random Sample Consensus”. In Proceedings of the Neural Information Processing: 21st International Conference, ICONIP, 454–461.
Kirillov, A., K. He, R. and C. R. Girshick. 2019. “Panoptic Segmentation”. In Proceedings of the IEEE/ CVF Conf. on Computer Vision and Pattern Recognition.
Lafuente-Arroyo, S. Gil-Jimenez, P.; Maldonado-Bascon, R. López-Ferreras, F. and S. Maldonado-Bascon. 2005. “Traffic Sign Shape Classification Evaluation I: SVM Using Distance to Borders”. In Proceedings of the IEEE. Intelligent Vehicles Symposium. 557–562.
Lange, S. Ulbrich, F. Goehring, D. 2022. “Online Vehicle Detection Using Deep Neural Networks and Lidar Based Preselected Image Patches”. IEEE Intelligent Vehicles Symposium (IV).
Laroca, R. Severo, E. Zanlorensi, L.A. Oliveira, L.S. Gonçalves, G.R. 2018. “A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector”. International joint Conference on Neural Networks (IJCNN): 1–10.
Le, T.T. Tran, S.T. Mita, S. T.D. Nguyen. 2010. “Real Time Traffic Sign Detection Using Color and Shape-Based Features”. In Proceedings of the ACIIDS: 268–278.
Lee, S. Kim, J. Shin Yoon, J. 2017. Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition. IEEE International Conference on Computer Vision.
Li, C. and C. Yang. 2016. “The Research on Traffic Sign Recognition Based on Deep Learning”. In Proceedings of the 2016 16th International Symposium on Communications and Information Technologies (ISCIT), 156–161.
Li, J. Mei, X. Prokhorov, D. Tao, D. 2016. “Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene”. IEEE Trans. Neural Networks.
Li, L. Lv, Y. and F.-Y Wang. 2016. “Traffic Signal Timing via Deep Reinforcement Learning”. IEEE/ CAA J. 3: 247–254.
Liu, Y. Xu, P. Zhu, L. Yan, M. L Xue. 2022. “Reinforced Attention Method for Real-Time Traffic Line Detection”. J. Real Time Image Process., 19: 957–968.
Maldonado-Bascón, S. Lafuente-Arroyo, S. Gil-Jimenez, P. Gómez-Moreno, H. and F. López-Ferreras. 2007. “Road-Sign Detection and Recognition Based on Support Vector Machines”. IEEE Trans. Intell. Transp. Syst., 8; 264–278.
Mamun, A. Ping, E.P. Hossen, J. Tahabilder, A. Jahan, B. 2022. “A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks”. Sensors: 671–682.
Maria, J. Amaro, G. L.A. Alexandre.2016. “Stacked Autoencoders Using Low-Power Accelerated Architectures for Object Recognition in Autonomous Systems”. Neural Process. 43: 445–458.
Marques, R. Ribeiro, T. Lopes, G. Ribeiro, A.F. 2022. “YOLOv3: Traffic Signs & Lights Detection and Recognition for Autonomous Driving”. In Proceedings of the ICAART (3): 818–826.
Messelodi, S. Modena, N., M. Zanin. 2005. “A Kalman Filter Based Background Updating Algorithm Robust to Sharp Illumination Changes”. In Proc. of ICIAP, 3617: 163–170.
Mittal, D. Reddy, A. Ramadurai, G. Mitra, K. and B. Ravindran 2018. “Training a Deep Learning Architecture for Vehicle Detection Using Limited Heterogeneous Traffic Data”. In Proceedings of the 2018 10th Int. Conf. on Communication Systems & Networks. 294–589.
Mufti, N. Shah, S.A.A. 2021. “Automatic Number Plate Recognition: A Detailed Survey of Relevant Algorithms”. Sensors, 21: 3028.
Okutani, I. Y.J. Stephanedes. 1984. “Dynamic Prediction of Traffic Volume through Kalman Filtering Theory”. Transp. Res. Part B: 18, 1–11.
Oren, M. Papageorgiou, C. Sinha, 1997. “Pedestrian Detection Using Wavelet Templates”. IEEE COMPUTER society Conference on Computer Vision and Pattern Recognition.
Ouyang, W. Wang, X. 2013. “Joint Deep Learning for Pedestrian Detection”. In Proceedings of the IEEE International Conference on Computer Vision, 2056–2063.
Papageorgiou, C. Evgeniou, T. and T. Poggio. 1998. “A Trainable Pedestrian Detection System”. In Proceedings of the Proc. of Intelligent Vehicles, 241–246.
Pillai, U.K.K. Valles, D. 2021. “An Initial Deep CNN Design Approach for Identification of Vehicle Color and Type for Amber and Silver Alerts”. In Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop.
Pustokhina, I.V. Pustokhin, D.A., J.J.P.C Rodrigues. 2020. “Automatic Vehicle LPR Using Optimal K-Means with Convolutional Neural Network for Intelligent Transportation Systems.
Qian, R. Zhang, B. Yue, Y. Wang, Z. Coenen, F. 2015. “Robust Chinese Traffic Sign Detection and Recognition with Deep Convolutional Neural Network”. In Proceedings of the 2015 11th International Conference on Natural Computation, 791–796.
Schlosser, J. Chow, C.K. Kira, Z. 2016. “Fusing Lidar and Images for Pedestrian Detection Using Convolutional Neural Networks”. In Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2198–2205.
Shan, B. Zheng, S. and J.Ou. 2016. “A Stereovision-Based Crack Width Detection Approach for Concrete Surface Assessment”. KSCE J. Civ. Eng., 20: 803–812.
Shvai, N. Hasnat, A. Meicler, A. Nakib, A. 2019. “Accurate Classification for Automatic Vehicle-Type Recognition Based on Ensemble Classifiers”. IEEE Trans.
Sindagi, V.A. Patel, V.M. 2017. “A Survey of Recent Advances in CNN-Based Single Image Crowd Counting and Density Estimation”. Pattern Recognit. Lett., 107: 3–16.
Song, J. Zhang, H. Li, X. GAO, L. Wang, M. R. Hong. 2018. “Self-Supervised Video Hashing with Hierarchical Binary Auto-Encoder”. IEEE Trans. Image Process: 3210–3221.
Song, X. R. Nevatia. 2007. “Detection and Tracking of Moving Vehicles in Crowded Scenes”. In Proceedings of the IEEE Workshop on Motion and Video Computing.
Theis, L., Shi, W., Cunningham, A., & Huszár, F. 2017. Lossy Image Compression with Compressive Autoencoders. ArXiv, abs/1703.00395.
Tian, Y. Luo, P. Wang, X. Tang, X. 2015. “Deep Learning Strong Parts for Pedestrian Detection”. In Proceedings of the IEEE International Conference on Computer Vision.
Tripathi, G. Singh, K. Vishwakarma, D.K. 2018. “Convolutional Neural Networks for Crowd Behaviour Analysis: A Survey”. Vis. Comput., 35: 753–776.
Tuzel, O. Porikli, F. Meer, P. 2008. Pedestrian Detection via Classification on Riemannian Manifolds. IEEE Trans. Pattern Anal. Mach. Intell., 30, 1713–1727.
Varshney, H., R.A. Khan, and U. Khan. 2021. Approaches of Artificial Intelligence and Machine Learning in Smart Cities: Critical Review.
Vetriselvi, T. Lydia, E.L. Mohanty, S.N. Alabdulkreem, E. 2022. “Deep Learning Based License Plate Number Recognition for Smart Cities”. CMC Comput. Mater Contin., 70: 2049–2064.
Vishal, K. Arvind, and V. Gundimeda. 2018. “Traffic Light Recognition for Autonomous Vehicles by Admixing the Traditional ML and DL”. In Proc. of Int. Conf. on Machine Vision.
Wang, X., Y. Zhang. 2017. “The Detection and Recognition of Bridges’ Cracks Based on Deep Belief Network”. IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).
Wang, Y., D. Zhang, Y. Liu, Dai. 2019. “Enhancing Transportation Systems via Deep Learning: A Survey”. Transp. Res. Part C Emerg. Technol. 99: 144–163.
Wu, B. Nevatia, R. 2007. “Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet Based Part Detectors”. Int. Journal of Compututer. 75: 247–26
Wu, L.-T. Lin, H.-Y. 2018. “Overtaking Vehicle Detection Techniques Based on Optical Flow and Convolutional Neural Network”. In Proceedings of the VEHITS.
Xing, J. Nguyen, M. Qi Yan, W. 2022. “The Improved Framework for Traffic Sign Recognition Using Guided Image Filtering”. SN Comput. Sci., 3: 461.
Yi, S. 2016. Pedestrian Behavior Modeling and Understanding in Crowds. Doctoral Dissertation. The Chinese University of Hong Kong.
Zang, X. Li, G. GAO, W. 2022. “Multidirection and Multiscale Pyramid in Transformer for Video-Based Pedestrian Retrieval”. IEEE Trans. 18: 8776–8785.
Zeng, Y. Xu, X. Shen, D. Fang, Y. Xiao, Z. 2016. “Traffic Sign Recognition Using Kernel Extreme Learning Machines with Deep Perceptual Features”. IEEE Trans. Intell. Syst., 18: 647–653.
Zhang, J. Huang, Q. Wu, H. Liu, Y. 2017. “A Shallow Network with Combined Pooling for Fast Traffic Sign Recognition”. Information: 8–45.
Zhang, S. Yang, J. Schiele, B. 2018. “Occluded Pedestrian Detection through Guided Attention in Cnns”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 6995–7003.
Zhao, L. Thorpe, C.E. 2000. “Stereo-and Neural Network-Based Pedestrian Detection”. IEEE Trans. Intell. Transp. Syst., 1: 148–154.
Zhu, J. Li, X. Jin, P. Xu, Q. Sun, Z. Song, X. 2020. “Mme-Yolo: Multi-Sensor Multi-Level Enhanced Yolo for Robust Vehicle Detection in Traffic Surveillance”. Sensors.
