{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:27:09Z","timestamp":1760239629831,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T00:00:00Z","timestamp":1607040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20180236"],"award-info":[{"award-number":["BK20180236"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traffic scene construction and simulation has been a hot topic in the community of intelligent transportation systems. In this paper, we propose a novel framework for the analysis and synthesis of traffic elements from road image sequences. The proposed framework is composed of three stages: traffic elements detection, road scene inpainting, and road scene reconstruction. First, a new bidirectional single shot multi-box detector (BiSSD) method is designed with a global context attention mechanism for traffic elements detection. After the detection of traffic elements, an unsupervised CycleGAN is applied to inpaint the occlusion regions with optical flow. The high-quality inpainting images are then obtained by the proposed image inpainting algorithm. Finally, a traffic scene simulation method is developed by integrating the foreground and background elements of traffic scenes. The extensive experiments and comparisons demonstrate the effectiveness of the proposed framework.<\/jats:p>","DOI":"10.3390\/s20236939","type":"journal-article","created":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T11:59:00Z","timestamp":1607083140000},"page":"6939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Analysis and Synthesis of Traffic Scenes from Road Image Sequences"],"prefix":"10.3390","volume":"20","author":[{"given":"Sheng","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuting","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huihui","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Y., Cui, Z., Liu, Y., Zhu, J., Zhao, D., and Jian, Y. (2018). Road scene simulation based on vehicle sensors: An intelligent framework using random walk detection and scene stage reconstruction. Sensors, 18.","DOI":"10.3390\/s18113782"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TPAMI.2007.60","article-title":"Space-time completion of video","volume":"29","author":"Wexler","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Horry, Y., Anjyo, K., and Arai, K. (1997, January 3\u20138). Tour into the picture: Using a spidery interface to make animation from a single image. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques (ACM Siggraph 97 Conference), Los Angeles, CA, USA. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.211.8170.","DOI":"10.1145\/258734.258854"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MC.2010.170","article-title":"Google street view: Capturing the world at street level","volume":"43","author":"Anguelov","year":"2010","journal-title":"Computer"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1109\/TITS.2011.2159493","article-title":"Cognitive cars: A new frontier for ADAS research","volume":"13","author":"Li","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 8\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the ECCV 2016, Amsterdam, The Netherlands."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Villager, E., Aubert, G., and Blanc-Feraud, L. (2004, January 26). Image disocclusion using a probabilistic gradient orientation. Proceedings of the 17th International Conference on Pattern Recognition, ICPR, Cambridge, UK.","DOI":"10.1109\/ICPR.2004.1334034"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nitzberg, M., Mumford, D., and Shiota, T. (1993). Filtering, Segmentation and Depth, Springer.","DOI":"10.1007\/3-540-56484-5"},{"key":"ref_11","unstructured":"Masnou, S., and Morel, J.-M. (1998, January 4\u20137). Level lines based disocclusion. Proceedings of the 1998 International Conference on Image, Chicago, IL, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bertalmio, M., Sapiro, G., Caselles, V., and Ballester, C. (2000, January 23\u201328). Image inpainting. Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, New Orleans, LA, USA.","DOI":"10.1145\/344779.344972"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., and Donahue, J. (2016, January 27\u201330). Context encoders: Feature learning by inpainting. Proceedings of the CVPR 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref_14","unstructured":"Xie, J., Xu, L., and Chen, E. (2012, January 3\u20136). Image denoising and inpainting with deep neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/versions?doi=10.1.1.421.2977."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, S., Yang, J., and Yang, M. (2017, January 21\u201326). Generative face completion. Proceedings of the CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.624"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1109\/TITS.2015.2497408","article-title":"Three-dimensional traffic scenes simulation from road image sequences","volume":"17","author":"Li","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","unstructured":"Lakshmi, T.R.V., and Reddy, C.V.K. (2019). Object Classification Using SIFT Algorithm and Transformation Techniques, Springer."},{"key":"ref_19","unstructured":"Lienhart, R., and Maydt, J. (2002, January 22\u201325). An extended set of Haar -like features for rapid object detection. Proceedings of the 2002 IEEE International Conference on Image Processing (ICIP), New York, NY, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Al Jarouf, Y.A., and Kurdy, M.B. (2018, January 25\u201326). A hybrid method to detect and verify vehiclecrash with haar-like features and SVM over the web. Proceedings of the International Conference on Computer and Applications (ICCA), Beirut, Lebanon. Available online: https:\/\/ieeexplore.ieee.org\/document\/8460417\/.","DOI":"10.1109\/COMAPP.2018.8460417"},{"key":"ref_21","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201326). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision Pattern Recognition, San Diego, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.measurement.2018.02.008","article-title":"Detection of power quality event using histogram of oriented gradients and support vector machine","volume":"120","author":"Kapoor","year":"2018","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.procs.2018.10.298","article-title":"Sensor based human activity recognition using adaboost ensemble classifier","volume":"140","author":"Subasi","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1007\/s00521-016-2818-2","article-title":"A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture","volume":"30","author":"Faris","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, S.F., Wen, L.Y., Bian, X., Lei, Z., and Li, S.Z. (2018, January 18\u201323). Single-Shot Refinement Neural Network for Object Detection. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00442"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, D., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T., and Le, Q.V. (2019, January 15\u201320). NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_32","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada. Available online: http:\/\/dl.acm.org\/citation.cfm?id=2969125."},{"key":"ref_33","unstructured":"Diganta, M. (2019). Mish: A Self Regularized Non-Monotonic Activation Function. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., and Hu, H. (2019, January 27\u201328). GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"ref_35","first-page":"2999","article-title":"Focal loss for dense object detection","volume":"99","author":"Lin","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","unstructured":"(2020, December 03). Available online: http:\/\/trafficdata.xjtu.edu.cn\/index.do."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6939\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:41:42Z","timestamp":1760179302000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,4]]},"references-count":36,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20236939"],"URL":"https:\/\/doi.org\/10.3390\/s20236939","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,12,4]]}}}