{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:14:57Z","timestamp":1771485297730,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,5,30]],"date-time":"2019-05-30T00:00:00Z","timestamp":1559174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Vehicle speed estimation is an important problem in traffic surveillance. Many existing approaches to this problem are based on camera calibration. Two shortcomings exist for camera calibration-based methods. First, camera calibration methods are sensitive to the environment, which means the accuracy of the results are compromised in some situations where the environmental condition is not satisfied. Furthermore, camera calibration-based methods rely on vehicle trajectories acquired by a two-stage tracking and detection process. In an effort to overcome these shortcomings, we propose an alternate end-to-end method based on 3-dimensional convolutional networks (3D ConvNets). The proposed method bases average vehicle speed estimation on information from video footage. Our methods are characterized by the following three features. First, we use non-local blocks in our model to better capture spatial\u2013temporal long-range dependency. Second, we use optical flow as an input in the model. Optical flow includes the information on the speed and direction of pixel motion in an image. Third, we construct a multi-scale convolutional network. This network extracts information on various characteristics of vehicles in motion. The proposed method showcases promising experimental results on commonly used dataset with mean absolute error (MAE) as 2.71 km\/h and mean square error (MSE) as 14.62 .<\/jats:p>","DOI":"10.3390\/fi11060123","type":"journal-article","created":{"date-parts":[[2019,5,30]],"date-time":"2019-05-30T11:07:44Z","timestamp":1559214464000},"page":"123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks"],"prefix":"10.3390","volume":"11","author":[{"given":"Huanan","family":"Dong","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Anhui 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Anhui 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhouwang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Anhui 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.ijleo.2013.06.036","article-title":"Vehicle speed measurement based on gray constraint optical flow algorithm","volume":"125","author":"Lan","year":"2014","journal-title":"Optik-Int. J. Light Electron Opt."},{"key":"ref_2","unstructured":"Mathew, T. (2014). Intrusive and Non-Intrusive Technologies, Indian Institute of Technology Bombay. Tech. Rep."},{"key":"ref_3","first-page":"1393","article-title":"A video-based system for vehicle speed measurement in urban roadways","volume":"18","author":"Luvizon","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","unstructured":"Huang, T. (2018, January 18\u201322). Traffic speed estimation from surveillance video data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nurhadiyatna, A., Hardjono, B., Wibisono, A., Sina, I., Jatmiko, W., Ma\u2019sum, M.A., and Mursanto, P. (2013, January 28\u201329). Improved vehicle speed estimation using gaussian mixture model and hole filling algorithm. Proceedings of the 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Bali, Indonesia.","DOI":"10.1109\/ICACSIS.2013.6761617"},{"key":"ref_6","first-page":"159","article-title":"A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD\u2013ARIMA framework","volume":"4","author":"Wang","year":"2016","journal-title":"Transportmet. B Transp. Dyn."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Maduro, C., Batista, K., Peixoto, P., and Batista, J. (2008, January 12\u201415). Estimation of vehicle velocity and traffic intensity using rectified images. Proceedings of the 2008 15th IEEE International Conference on Image Processing, San Diego, CA, USA.","DOI":"10.1109\/ICIP.2008.4711870"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sina, I., Wibisono, A., Nurhadiyatna, A., Hardjono, B., Jatmiko, W., and Mursanto, P. (2013, January 28\u201329). Vehicle counting and speed measurement using headlight detection. Proceedings of the 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Bali, Indonesia.","DOI":"10.1109\/ICACSIS.2013.6761567"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/6979.880967","article-title":"An algorithm to estimate mean traffic speed using uncalibrated cameras","volume":"1","author":"Dailey","year":"2000","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","unstructured":"Grammatikopoulos, L., and Karras, G. (2006, January 9\u201310). Automatic estimation of vehicle speed from uncalibrated video sequences. Proceedings of the FIG-ISPRS-ICA International Symposium on Modern Technologies, Education & Professional Practice in Geodesy & Related Fields, Sofia, Bulgaria."},{"key":"ref_11","unstructured":"Nam, H., Baek, M., and Han, B. (2016). Modeling and propagating cnns in a tree structure for visual tracking. arXiv."},{"key":"ref_12","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S.J. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Carreira, J., and Zisserman, A. (2017, January 21\u201326). Quo vadis, action recognition? A new model and the kinetics dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, X.C., and Yung, N.H. (2007, January 21\u201322). A novel algorithm for estimating vehicle speed from two consecutive images. Proceedings of the 2007 IEEE Workshop on Applications of Computer Vision (WACV \u201907), Austin, TX, USA.","DOI":"10.1109\/WACV.2007.7"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.neucom.2015.09.132","article-title":"An accurate and practical calibration method for roadside camera using two vanishing points","volume":"204","author":"You","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"037202","DOI":"10.1117\/1.2714991","article-title":"New method for overcoming ill-conditioning in vanishing-point-based camera calibration","volume":"46","author":"He","year":"2007","journal-title":"Opt. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kumar, A., Khorramshahi, P., Lin, W.A., Dhar, P., Chen, J.C., and Chellappa, R. (2018, January 18\u201322). A semi-automatic 2D solution for vehicle speed estimation from monocular videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2018, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00026"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple online and realtime tracking with a deep association metric. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_20","first-page":"8","article-title":"Automatic Camera Calibration for Traffic Understanding","volume":"4","author":"Herout","year":"2014","journal-title":"BMVC"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cviu.2017.05.015","article-title":"Traffic surveillance camera calibration by 3d model bounding box alignment for accurate vehicle speed measurement","volume":"161","author":"Sochor","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_22","unstructured":"Filipiak, P., Golenko, B., and Dolega, C. (April, January 30). NSGA-II Based Auto-Calibration of Automatic Number Plate Recognition Camera for Vehicle Speed Measurement. Proceedings of the European Conference on the Applications of Evolutionary Computation, Porto, Portugal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TITS.2018.2825609","article-title":"Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement","volume":"20","author":"Sochor","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 11\u201318). Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Pinz, A., and Wildes, R. (2016, January 5\u201310). Spatiotemporal residual networks for video action recognition. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain.","DOI":"10.1109\/CVPR.2017.787"},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A. (2014, January 8\u201313). Two-stream convolutional networks for action recognition in videos. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_28","unstructured":"Tran, D., Ray, J., Shou, Z., Chang, S.F., and Paluri, M. (2017). Convnet architecture search for spatiotemporal feature learning. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Yao, T., and Mei, T. (2017, January 22\u201329). Learning spatio-temporal representation with pseudo-3d residual networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.590"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., and Paluri, M. (2018, January 18\u201322). A Closer Look at Spatiotemporal Convolutions for Action Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2018, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00675"},{"key":"ref_31","unstructured":"Buades, A., Coll, B., and Morel, J.M. (2005, January 20\u201326). A non-local algorithm for image denoising. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image denoising by sparse 3-D transform-domain collaborative filtering","volume":"16","author":"Dabov","year":"2007","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_33","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2017). Non-local neural networks. arXiv."},{"key":"ref_34","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_35","unstructured":"Farneb\u00e4ck, G. (July, January 29). Two-frame motion estimation based on polynomial expansion. Proceedings of the Scandinavian Conference on Image Analysis, Halmstad, Sweden."},{"key":"ref_36","unstructured":"Loshchilov, I., and Hutter, F. (2017). Fixing weight decay regularization in adam. arXiv."},{"key":"ref_37","unstructured":"Burton, A., and Radford, J. (1978). Thinking in Perspective: Critical Essays in the Study of Thought Processes, Routledge."},{"key":"ref_38","unstructured":"Warren, D.H., and Strelow, E.R. (2013). Electronic Spatial Sensing for the Blind: Contributions from Perception, Rehabilitation, and Computer Vision, Springer Science & Business Media."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9821","DOI":"10.1016\/j.eswa.2012.02.171","article-title":"Toward reducing failure risk in an integrated vehicle health maintenance system: A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS","volume":"39","author":"Rodger","year":"2012","journal-title":"Exp. Syst. Appl."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/6\/123\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:52Z","timestamp":1760187292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/6\/123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,30]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["fi11060123"],"URL":"https:\/\/doi.org\/10.3390\/fi11060123","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,30]]}}}