{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T07:31:05Z","timestamp":1782977465755,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T00:00:00Z","timestamp":1569801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this manuscript, a new method for the determination of vehicle trajectories using an optimal bounding box for the vehicle is developed. The vehicle trajectory is extracted using images acquired from a camera installed at an intersection based on a convolutional neural network (CNN). First, real-time vehicle object detection is performed using the YOLOv2 model, which is one of the most representative object detection algorithms based on CNN. To overcome the inaccuracy of the vehicle location extracted by YOLOv2, the trajectory was calibrated using a vehicle tracking algorithm such as a Kalman filter and intersection-over-union (IOU) tracker. In particular, we attempted to correct the vehicle trajectory by extracting the center position based on the geometric characteristics of a moving vehicle according to the bounding box. The quantitative and qualitative evaluations indicate that the proposed algorithm can detect the trajectories of moving vehicles better than the conventional algorithm. Although the center points of the bounding boxes obtained using the existing conventional algorithm are often outside of the vehicle due to the geometric displacement of the camera, the proposed technique can minimize positional errors and extract the optimal bounding box to determine the vehicle location.<\/jats:p>","DOI":"10.3390\/s19194263","type":"journal-article","created":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T13:16:41Z","timestamp":1569849401000},"page":"4263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Determination of Vehicle Trajectory through Optimization of Vehicle Bounding Boxes using a Convolutional Neural Network"],"prefix":"10.3390","volume":"19","author":[{"given":"Seonkyeong","family":"Seong","sequence":"first","affiliation":[{"name":"CAL Lab., HyperSensing Inc., Yuseong-gu, gwahak-ro, Daejeon 169-84, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeongheon","family":"Song","sequence":"additional","affiliation":[{"name":"CAL Lab., HyperSensing Inc., Yuseong-gu, gwahak-ro, Daejeon 169-84, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghyeon","family":"Yoon","sequence":"additional","affiliation":[{"name":"CAL Lab., HyperSensing Inc., Yuseong-gu, gwahak-ro, Daejeon 169-84, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyoung","family":"Kim","sequence":"additional","affiliation":[{"name":"CAL Lab., HyperSensing Inc., Yuseong-gu, gwahak-ro, Daejeon 169-84, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3967-6481","authenticated-orcid":false,"given":"Jaewan","family":"Choi","sequence":"additional","affiliation":[{"name":"CAL Lab., HyperSensing Inc., Yuseong-gu, gwahak-ro, Daejeon 169-84, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, Y. (2018, January 25\u201326). Big Data Technology and its Analysis of Application in Urban Intelligent Transportation System. Proceedings of the International Conference on Intelligent Transportation\u2014Big Data Smart City, Xiamen, China.","DOI":"10.1109\/ICITBS.2018.00012"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1109\/TVT.2004.836889","article-title":"Sensing of passing vehicles using a lane marker on road with a built-in thin film MI sensor and power source","volume":"53","author":"Nishibe","year":"2004","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1109\/TMAG.2002.806351","article-title":"Magneto-impedance effect of a layered CoNbZr amorphous film formed on a polyimide substrate","volume":"39","author":"Nishibe","year":"2003","journal-title":"IEEE Trans. Magn."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/S0924-4247(99)00091-6","article-title":"An integrating magnetic sensor based on the giant magneto-impedance effect","volume":"81","author":"Atkinson","year":"2000","journal-title":"Sens. Actuators A Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28665","DOI":"10.3390\/s151128665","article-title":"Recent developments of magnetoresistive sensors for industrial applications","volume":"15","author":"Jogschies","year":"2015","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13815","DOI":"10.3390\/s140813815","article-title":"High-sensitivity low-noise miniature fluxgate magnetometers using a flip chip conceptual design","volume":"14","author":"Lu","year":"2014","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5247","DOI":"10.1109\/ACCESS.2018.2791446","article-title":"Improved robust vehicle detection and identification based on single magnetic sensor","volume":"6","author":"Dong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Marszalek, Z., Zeglen, T., Sroka, R., and Gajda, J. (2018). Inductive loop axle detector based on resistance and reactance vehicle magnetic profiles. Sensors, 18.","DOI":"10.3390\/s18072376"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TITS.2006.890070","article-title":"A traffic accident recording and reporting model at intersections","volume":"8","author":"Ki","year":"2007","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zou, Y., Shi, H., and Zhao, H. (2009, January 12\u201314). Video Image Vehicle Detection System for Signaled Traffic Intersection. Proceedings of the Ninth International Conference on Hybrid Intelligent Systems, Shenyang, China.","DOI":"10.1109\/HIS.2009.51"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TPAMI.2002.1033221","article-title":"An HMM-based segementation method for traffic monitoring movies","volume":"24","author":"Kato","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/6979.880969","article-title":"Image analysis and rule-based reasoning for a traffic monitoring system","volume":"1","author":"Cucchiara","year":"2000","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TVT.2006.883735","article-title":"Moving vehicle detection for automatic traffic monitoring","volume":"56","author":"Zhou","year":"2007","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, J., and Sun, M. (December, January 30). A YOLO-based Traffic Counting System. Proceedings of the 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI), Taichung, Taiwan.","DOI":"10.1109\/TAAI.2018.00027"},{"key":"ref_16","first-page":"2169","article-title":"Multi-scale detector for accurate vehicle detection in traffic surveillance data","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Forero, A., and Calderon, F. (2019, January 24\u201326). Vehicle and Pedestrian Video-Tracking with Classification Based on Deep Convolutional Neural Networks. Proceedings of the 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), Bucaramanga, Colombia.","DOI":"10.1109\/STSIVA.2019.8730234"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Asha, C.S., and Narasimhadhan, A.V. (2018, January 16\u201317). Vehicle Counting for Traffic Management System Using YOLO and Correlation Filter. Proceedings of the 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT.2018.8482380"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, F., Li, C., and Yang, F. (2019). Vehicle detection in urban traffic surveillance images based on convolutional neural networks with feature concatenation. Sensors, 19.","DOI":"10.3390\/s19030594"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, Z., Shi, H., Li, N., Xiang, C., and Zhou, H. (2018, January 10\u201312). Vehicle Detection Under UAV Based on Optimal Dense YOLO Method. Proceedings of the 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China.","DOI":"10.1109\/ICSAI.2018.8599403"},{"key":"ref_21","first-page":"1","article-title":"Lightweight deep network for traffic sign classifiction","volume":"74","author":"Zhang","year":"2019","journal-title":"Ann. Telecommun."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, J., Jin, X., Sun, J., Wang, J., and Sangaiah, A.K. (2018). Spatial and semantic convolutional features for robust visual object tracking. Multimedia Tools Appl., 1\u201321.","DOI":"10.1007\/s11042-018-6562-8"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"43956","DOI":"10.1109\/ACCESS.2019.2908668","article-title":"Dual model learning combined with multiple feature selection for accurate visual tracking","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"83873","DOI":"10.1109\/ACCESS.2019.2924944","article-title":"Spatially attentive visual tracking using multi-model adaptive response fusion","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","unstructured":"Koller, D., Weber, J., Huang, T., Malik, J., Ogasawara, G., Rao, B., and Russel, S. (1994, January 14\u201316). Towards Robust Automatic Traffic Scene Analysis in Real-Time. Proceedings of the 33rd Conference on Decision and Control, Lake Buena Vista, FL, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1049\/iet-its.2018.5365","article-title":"Deep learning-based vehicle detection with synthetic image data","volume":"13","author":"Wang","year":"2019","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sang, J., Wu, Z., Guo, P., Hu, H., Xiang, H., Zhang, Q., and Cai, B. (2018). An improved YOLOv2 for vehicle detection. Sensors, 18.","DOI":"10.3390\/s18124272"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, S., Zhang, F., Li, E., Yang, T., and Lu, Z. (2019). An adaptive framework for multi-vehicle ground speed estimation in airborne videos. Remote Sens., 11.","DOI":"10.3390\/rs11101241"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3172","DOI":"10.1109\/TCYB.2017.2705345","article-title":"Trajectory predictor by using recurrent neural networks in visual tracking","volume":"47","author":"Wang","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_30","unstructured":"Brown, D.C. (August, January 28). Close-Range Camera Calibration. Proceedings of the Symposium on Close-Range Photogrammetry System, ISPRS, Chicago, IL, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/TPAMI.1987.4767922","article-title":"New methods for matching 3-D objects with single perspective view","volume":"9","author":"Horaud","year":"1987","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van Gool, L. (2006, January 20\u201324). Efficient Non-Maximum Suppression. Proceedings of the International Conference on Pattern Recognition (ICPR), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_35","unstructured":"Ioffe, S., and Szegedy, C. (2005, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/MSP.2012.2203621","article-title":"Understanding the basis of the kalman filter via a simple and intuitive derivation","volume":"29","author":"Faragher","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/34.771328","article-title":"Robust tracking of position and velocity with Kalman snakes","volume":"21","author":"Peterfreund","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A new approach to linear filtering and prediction problems","volume":"82","author":"Kalman","year":"1960","journal-title":"Trans. ASME J. Basic Eng."},{"key":"ref_39","unstructured":"Bochinski, E., Eiselein, V., and Sikora, T. (September, January 29). High-Speed Tracking-by-Detection without Using Image Information. Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, Lecce, Italy."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4263\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:26:26Z","timestamp":1760189186000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,30]]},"references-count":39,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194263"],"URL":"https:\/\/doi.org\/10.3390\/s19194263","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,30]]}}}