{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T11:24:47Z","timestamp":1777461887746,"version":"3.51.4"},"reference-count":24,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Intelligent traffic recognition system is the development direction of the future traffic system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology into the entire ground traffic management system. It establishes a real-time, accurate, and efficient integrated transportation management system that plays a role in a wide range and all directions. The aim of this article is to integrate cross-modal biometrics into an intelligent traffic recognition system combined with real-time data operations. Based on the cross-modal recognition algorithm, it can better re-identify the vehicle cross-modally by building a model. First, this article first presents a general introduction to the cross-modal recognition method. Then, the experimental analysis is conducted on the classification of vehicle images recognized by the intelligent transportation system, the complexity of vehicle logo recognition, and the recognition of vehicle images with different lights. Finally, the cross-modal recognition algorithm is introduced into the dynamic analysis of the intelligent traffic recognition system. The cross-modal traffic recognition system experiment is carried out. The experimental results show that the intraclass distribution loss function can improve the Rank 1 recognition rate and mAP value by 6\u20137% points on the basis of the baseline method. This shows that improving the modal invariance feature by reducing the distribution difference between different modal images of the same vehicle can effectively deal with the feature information imbalance caused by modal changes.<\/jats:p>","DOI":"10.1515\/comp-2022-0252","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T16:07:29Z","timestamp":1666022849000},"page":"332-344","source":"Crossref","is-referenced-by-count":1,"title":["Cross-modal biometric fusion intelligent traffic recognition system combined with real-time data operation"],"prefix":"10.1515","volume":"12","author":[{"given":"Wei","family":"Xu","sequence":"first","affiliation":[{"name":"College of Modern Information Technology, Henan Polytechnic , Zhengzhou , 450000, Henan , China"}]},{"given":"Yujin","family":"Zhai","sequence":"additional","affiliation":[{"name":"College of Modern Information Technology, Henan Polytechnic , Zhengzhou , 450000, Henan , China"}]}],"member":"374","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"2022101716054318204_j_comp-2022-0252_ref_001","doi-asserted-by":"crossref","unstructured":"E. A. Abed, R. J. Mohammed, and T. Shihab, \u201cIntelligent multimodal identification system based on local feature fusion between iris and finger vein,\u201d Indonesian J. Electr. Eng. Comput. Sci., vol. 21, no. 1, pp. 224\u2013232, 2021.","DOI":"10.11591\/ijeecs.v21.i1.pp224-232"},{"key":"2022101716054318204_j_comp-2022-0252_ref_002","doi-asserted-by":"crossref","unstructured":"P. Punyani, R. Gupta, and A. Kumar, \u201cA multimodal biometric system using match score and decision level fusion,\u201d Int. J. Inf. Technol., vol. 14, no. 2, pp. 725\u2013730, 2022.","DOI":"10.1007\/s41870-021-00843-3"},{"key":"2022101716054318204_j_comp-2022-0252_ref_003","doi-asserted-by":"crossref","unstructured":"S. Aleem, P. Yang, S. Masood, P. Li, and B. Sheng, \u201c An accurate multi-modal biometric identification system for person identification via fusion of face and finger print,\u201d World Wide Web, vol. 23, no. 2, pp. 1299\u20131317, 2020.","DOI":"10.1007\/s11280-019-00698-6"},{"key":"2022101716054318204_j_comp-2022-0252_ref_004","doi-asserted-by":"crossref","unstructured":"K. Vasavi and Y. Latha, \u201cRSA cryptography based multi-modal biometric identification system for high-security application,\u201d Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 10\u201321, 2019.","DOI":"10.22266\/ijies2019.0228.02"},{"key":"2022101716054318204_j_comp-2022-0252_ref_005","doi-asserted-by":"crossref","unstructured":"M. W. Rahman, F. T. Zohra, and M. L. Gavrilova, \u201cScore level and rank level fusion for kinect-based multi-modal biometric system,\u201d J. Artif. Intell. Soft Comput. Res., vol. 9, no. 3, pp. 167\u2013176, 2019.","DOI":"10.2478\/jaiscr-2019-0001"},{"key":"2022101716054318204_j_comp-2022-0252_ref_006","doi-asserted-by":"crossref","unstructured":"H. Hamidi and A. Kamankesh, \u201cAn approach to intelligent traffic management system using a multi-agent system,\u201d Int. J. Intell. Transp. Syst. Res., vol. 16, no. 2, pp. 1\u201313, 2018.","DOI":"10.1007\/s13177-017-0142-6"},{"key":"2022101716054318204_j_comp-2022-0252_ref_007","doi-asserted-by":"crossref","unstructured":"S. Mohamed and K. A. Alshalfan, \u201cIntelligent traffic management system based on the internet of vehicles (IoV),\u201d J. Adv. Transp., vol. 2021, no. 4, pp. 1\u201323, 2021.","DOI":"10.1155\/2021\/4037533"},{"key":"2022101716054318204_j_comp-2022-0252_ref_008","doi-asserted-by":"crossref","unstructured":"M. Merouane, \u201cAn approach for detecting anonymized traffic: Orbot as case study,\u201d Autom. Control. Comput. Sci., vol. 56, no. 1, pp. 45\u201357, 2022.","DOI":"10.3103\/S0146411622010072"},{"key":"2022101716054318204_j_comp-2022-0252_ref_009","doi-asserted-by":"crossref","unstructured":"Z. Liu, R. Wang, and D. Tang, \u201cExtending labeled mobile network traffic data by three levels traffic identification fusion,\u201d Future Gener. Comput. Syst., vol. 88, no. NOV, pp. 453\u2013466, 2018.","DOI":"10.1016\/j.future.2018.05.079"},{"key":"2022101716054318204_j_comp-2022-0252_ref_010","unstructured":"R. Gayathri, M. A. Bhairavi, and D. Aravind, \u201cAn intelligent and real time system for automatic driven toll gate system under complex scenes,\u201d Int. J. Comput. Intell. Res., vol. 14, no. 1, pp. 1\u201313, 2018."},{"key":"2022101716054318204_j_comp-2022-0252_ref_011","doi-asserted-by":"crossref","unstructured":"R. Sathiyaraj and A. Bharathi, \u201cAn efficient intelligent traffic light control and deviation system for traffic congestion avoidance using multi-agent system,\u201d Transport, vol. 35, no. 3, pp. 1\u20139, 2019.","DOI":"10.3846\/transport.2019.11115"},{"key":"2022101716054318204_j_comp-2022-0252_ref_012","unstructured":"Z. Wang and Y. Ma, \u201cDetection and recognition of stationary vehicles and seat belts in intelligent Internet of Things traffic management system,\u201d Neural Comput. Appl., vol. 9, pp. 1\u201310, 2021."},{"key":"2022101716054318204_j_comp-2022-0252_ref_013","doi-asserted-by":"crossref","unstructured":"J. Wang, B. He, J. Wang, and T. Li, \u201cIntelligent VNFs selection based on traffic identification in vehicular cloud networks,\u201d IEEE Trans. Veh. Technol., vol. 68, no. 5, pp. 4140\u20134147, 2019.","DOI":"10.1109\/TVT.2018.2880754"},{"key":"2022101716054318204_j_comp-2022-0252_ref_014","unstructured":"D. Sivabalaselvamani, \u201cReal time traffic flow prediction and intelligent traffic control from remote location for large-scale heterogeneous networking using tensorflow,\u201d Int. J. Future Gener. Commun. Netw., vol. 13, no. 1, pp. 1006\u20131012, 2020."},{"key":"2022101716054318204_j_comp-2022-0252_ref_015","doi-asserted-by":"crossref","unstructured":"Z. Liu and C. Wang, \u201cDesign of traffic emergency response system based on internet of things and data mining in emergencies,\u201d IEEE Access, vol. 7, no. 99, pp. 113950\u2013113962, 2019.","DOI":"10.1109\/ACCESS.2019.2934979"},{"key":"2022101716054318204_j_comp-2022-0252_ref_016","unstructured":"S. M. Rajalaksh, A. Deborah, R. S. Thiru, K. Priya, and M. Rajendram, \u201cRFID-based traffic violation detection and traffic flow analysis system,\u201d Int. J. Pure Appl. Math., vol. 118, no. 20, pp. 319\u2013328, 2018."},{"key":"2022101716054318204_j_comp-2022-0252_ref_017","doi-asserted-by":"crossref","unstructured":"H. V. Chand, and J. Karthikeyan, \u201cSurvey on the role of IoT in intelligent transportation system,\u201d Indonesian J. Electr. Eng. Comput. Sci., vol. 11, no. 3, pp. 936\u2013941, 2018.","DOI":"10.11591\/ijeecs.v11.i3.pp936-941"},{"key":"2022101716054318204_j_comp-2022-0252_ref_018","doi-asserted-by":"crossref","unstructured":"D. L. Dinh, H. N. Nguyen, H. T. Thai, and K. H. Le, \u201cTowards AI-based traffic counting system with edge computing,\u201d J. Adv. Transp., vol. 2021, no. 2, pp. 1\u201315, 2021.","DOI":"10.1155\/2021\/5551976"},{"key":"2022101716054318204_j_comp-2022-0252_ref_019","doi-asserted-by":"crossref","unstructured":"K. Bhagavan, S. S. Saketh, G. Mounika, M. Vishal, and M. Hemanth, \u201cIOT based intelligent street lighting system for smart city,\u201d Int. J. Eng. Technol., vol. 7, no. 2, pp. 345\u2013347, 2018.","DOI":"10.14419\/ijet.v7i2.32.15709"},{"key":"2022101716054318204_j_comp-2022-0252_ref_020","doi-asserted-by":"crossref","unstructured":"M. M. Ahmed, M. Abdel-Aty, and R. Yu, \u201cBayesian updating approach for real-time safety evaluation with automatic vehicle identification data,\u201d Transp. Res. Rec., vol. 2280, no. 1, pp. 60\u201367, 2018.","DOI":"10.3141\/2280-07"},{"key":"2022101716054318204_j_comp-2022-0252_ref_021","doi-asserted-by":"crossref","unstructured":"A. Jenefa and B. S. Moses, \u201cA multi-phased statistical learning based classification for network traffic,\u201d J. Intell. Fuzzy Syst., vol. 40, no. 14, pp. 1\u201319, 2021.","DOI":"10.3233\/JIFS-201895"},{"key":"2022101716054318204_j_comp-2022-0252_ref_022","doi-asserted-by":"crossref","unstructured":"R. Thiagarajan and D. S. Prakashkumar, \u201cIdentification of passenger demand in public transport using machine learning,\u201d Webology, vol. 18, no. Special Issue 02, pp. 223\u2013236, 2021.","DOI":"10.14704\/WEB\/V18SI02\/WEB18068"},{"key":"2022101716054318204_j_comp-2022-0252_ref_023","unstructured":"B. H. Sun, W. W. Deng, B. Zhu, J. Wu, and S. S. Wang, \u201cIdentification of vehicle motion intention based on reaction behavior model,\u201d Jilin Daxue Xuebao (Gongxueban)\/Journal Jilin Univ. (Eng. Technol. Ed.), vol. 48, no. 1, pp. 36\u201343, 2018."},{"key":"2022101716054318204_j_comp-2022-0252_ref_024","doi-asserted-by":"crossref","unstructured":"G. Lee, R. Mallipeddi, and M. Lee, \u201cTrajectory-based vehicle tracking at low frame rates,\u201d Expert. Syst. Appl., vol. 80, no. SEP, pp. 46\u201357, 2017.","DOI":"10.1016\/j.eswa.2017.03.023"}],"container-title":["Open Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2022-0252\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2022-0252\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T16:07:44Z","timestamp":1666022864000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2022-0252\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,10,17]]},"published-print":{"date-parts":[[2022,10,17]]}},"alternative-id":["10.1515\/comp-2022-0252"],"URL":"https:\/\/doi.org\/10.1515\/comp-2022-0252","relation":{},"ISSN":["2299-1093"],"issn-type":[{"value":"2299-1093","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,1]]}}}