{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T11:14:48Z","timestamp":1771326888030,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,12,19]],"date-time":"2021-12-19T00:00:00Z","timestamp":1639872000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,12,19]],"date-time":"2021-12-19T00:00:00Z","timestamp":1639872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFB1600702"],"award-info":[{"award-number":["2019YFB1600702"]}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["6207072223"],"award-info":[{"award-number":["6207072223"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"national natural science funds for distinguished young scholars of china","award":["62006026"],"award-info":[{"award-number":["62006026"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Traffic incidents endanger the smooth running of vehicles. Congestion caused by traffic incidents has caused a waste of time and fuel and seriously affected transportation efficiency. At present, most methods use manual judgment or image features to detect traffic incidents, but these methods lack timeliness, leading to secondary incidents. For dangerous road sections such as ramp-free and long downhills, this paper proposes an algorithm to quickly detect traffic incidents based on a spatiotemporal map of vehicle trajectories. First, a vehicle dataset from the monitoring perspective is constructed, and an improved YOLOv4 detection algorithm is used to detect images organized as batches. Based on the detection result, the multi-object tracking method of vehicle speed prediction in key frames is used to obtain the vehicle trajectory. Then according to the vehicle trajectory obtained in a single scene, the vehicle trajectory is reidentified and associated in the continuous monitoring scene to construct a long-distance vehicle trajectory spatiotemporal map. Finally, according to the distribution and generation status of the trajectory in the spatiotemporal map, traffic incidents such as vehicle parking, vehicle speeding, and vehicle congestion are analyzed. Experimental results show that the proposed method greatly increases the speed of vehicle detection and tracking and obtains high mAP, MOTA, and MOTP indicators. The global spatiotemporal map constructed by trajectory reidentification can achieve high detection rates for traffic incidents, reduce the average elapsed time, and avoid the problems of the inaccuracy of analyzing image features.<\/jats:p>","DOI":"10.1007\/s40747-021-00602-8","type":"journal-article","created":{"date-parts":[[2021,12,19]],"date-time":"2021-12-19T07:02:16Z","timestamp":1639897336000},"page":"1389-1408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Traffic incident detection based on a global trajectory spatiotemporal map"],"prefix":"10.1007","volume":"8","author":[{"given":"Haoxiang","family":"Liang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8590-0061","authenticated-orcid":false,"given":"Huansheng","family":"Song","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Yun","sequence":"additional","affiliation":[]},{"given":"Shijie","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yingxuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhaoyang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,19]]},"reference":[{"key":"602_CR1","unstructured":"Wells T, Toffin E (2005) Video-based automatic incident detection on san-mateo bridge in the san francisco bay area. In: 12th World Congress on ITS, San Francisco, Citeseer"},{"issue":"2","key":"602_CR2","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1109\/TITS.2007.894193","volume":"8","author":"BM Williams","year":"2007","unstructured":"Williams BM, Guin A (2007) Traffic management center use of incident detection algorithms: findings of a nationwide survey. IEEE Trans Intell Transp Syst 8(2):351\u2013358","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"602_CR3","doi-asserted-by":"crossref","unstructured":"Naphade M, Chang MC, Sharma A, Anastasiu DC, Jagarlamudi V, Chakraborty P, Huang T, Wang S, Liu MY, Chellappa R et\u00a0al (2018) The 2018 nvidia ai city challenge. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 53\u201360","DOI":"10.1109\/CVPRW.2018.00015"},{"key":"602_CR4","doi-asserted-by":"crossref","unstructured":"Naphade M, Tang Z, Chang MC, Anastasiu DC, Sharma A, Chellappa R, Wang S, Chakraborty P, Huang T, Hwang JN et\u00a0al (2019) The 2019 ai city challenge. In: CVPR Workshops, vol\u00a08","DOI":"10.1109\/CVPRW50498.2020.00321"},{"issue":"1","key":"602_CR5","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/MSP.2017.2749125","volume":"35","author":"J Han","year":"2018","unstructured":"Han J, Zhang D, Cheng G, Liu N, Xu D (2018) Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Process Mag 35(1):84\u2013100","journal-title":"IEEE Signal Process Mag"},{"issue":"3","key":"602_CR6","first-page":"187","volume":"14","author":"C Ozkurt","year":"2009","unstructured":"Ozkurt C, Camci F (2009) Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks. Math Comput Appl 14(3):187\u2013196","journal-title":"Math Comput Appl"},{"key":"602_CR7","first-page":"3","volume":"43","author":"J Kotzenmacher","year":"2005","unstructured":"Kotzenmacher J, Minge ED, Hao B (2005) Evaluation of portable non-intrusive traffic detection system. IMSA J 43:3","journal-title":"IMSA J"},{"issue":"6","key":"602_CR8","first-page":"25","volume":"7","author":"M Zhong","year":"2007","unstructured":"Zhong M, Guoxin L (2007) Establishing and managing jurisdiction-wide traffic monitoring systems: North american experiences. J Transp Syst Eng Inf Technol 7(6):25\u201338","journal-title":"J Transp Syst Eng Inf Technol"},{"key":"602_CR9","doi-asserted-by":"crossref","unstructured":"Balcilar M, Sonmez AC (2008) Extracting vehicle density from background estimation using kalman filter. In: 2008 23rd international symposium on computer and information sciences, IEEE, pp 1\u20135","DOI":"10.1109\/ISCIS.2008.4717950"},{"issue":"6","key":"602_CR10","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"602_CR11","doi-asserted-by":"crossref","unstructured":"Onoro-Rubio D, L\u00f3pez-Sastre RJ (2016) Towards perspective-free object counting with deep learning. In: European conference on computer vision, Springer, pp 615\u2013629","DOI":"10.1007\/978-3-319-46478-7_38"},{"issue":"11","key":"602_CR12","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1016\/j.imavis.2013.09.006","volume":"31","author":"O Asmaa","year":"2013","unstructured":"Asmaa O, Mokhtar K, Abdelaziz O (2013) Road traffic density estimation using microscopic and macroscopic parameters. Image Vis Comput 31(11):887\u2013894","journal-title":"Image Vis Comput"},{"key":"602_CR13","first-page":"1324","volume":"23","author":"V Lempitsky","year":"2010","unstructured":"Lempitsky V, Zisserman A (2010) Learning to count objects in images. Adv Neural Inf Process Syst 23:1324\u20131332","journal-title":"Adv Neural Inf Process Syst"},{"key":"602_CR14","unstructured":"Gon\u00e7alves WN, Machado BB, Bruno OM (2012) Spatiotemporal gabor filters: a new method for dynamic texture recognition. arXiv:1201.3612"},{"key":"602_CR15","doi-asserted-by":"crossref","unstructured":"Yuan Z, Zhou X, Yang T (2018) Hetero-convlstm: a deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & Data Mining, pp 984\u2013992","DOI":"10.1145\/3219819.3219922"},{"issue":"10","key":"602_CR16","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1111\/mice.12376","volume":"33","author":"H Hashemi","year":"2018","unstructured":"Hashemi H, Abdelghany K (2018) End-to-end deep learning methodology for real-time traffic network management. Comput-Aided Civ Infrastruct Eng 33(10):849\u2013863","journal-title":"Comput-Aided Civ Infrastruct Eng"},{"key":"602_CR17","doi-asserted-by":"crossref","unstructured":"Zhu L, Guo F, Krishnan R, Polak JW (2018) A deep learning approach for traffic incident detection in urban networks. In: 2018 21st international conference on intelligent transportation systems (ITSC), IEEE, pp 1011\u20131016","DOI":"10.1109\/ITSC.2018.8569402"},{"key":"602_CR18","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"602_CR19","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"issue":"3","key":"602_CR20","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1109\/TITS.2018.2838132","volume":"20","author":"X Hu","year":"2018","unstructured":"Hu X, Xu X, Xiao Y, Chen H, He S, Qin J, Heng PA (2018) Sinet: a scale-insensitive convolutional neural network for fast vehicle detection. IEEE Trans Intell Transp Syst 20(3):1010\u20131019","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"602_CR21","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision, Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"602_CR22","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv:2004.10934"},{"key":"602_CR23","unstructured":"Purkait P, Zhao C, Zach C (2017) Spp-net: deep absolute pose regression with synthetic views. arXiv:1712.03452"},{"key":"602_CR24","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"602_CR25","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6023\u20136032","DOI":"10.1109\/ICCV.2019.00612"},{"key":"602_CR26","doi-asserted-by":"crossref","unstructured":"Chen Y, Zhang P, Li Z, Li Y, Zhang X, Meng G, Xiang S, Sun J, Jia J (2020) Stitcher: feedback-driven data provider for object detection. arXiv\u20132004","DOI":"10.1155\/2020\/8830731"},{"key":"602_CR27","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2021) Scaled-yolov4: scaling cross stage partial network. In: Proceedings of the IEEE\/cvf conference on computer vision and pattern recognition, pp 13029\u201313038","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"602_CR28","doi-asserted-by":"crossref","unstructured":"Chen Q, Wang Y, Yang T, Zhang X, Cheng J, Sun J (2021) You only look one-level feature. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13039\u201313048","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"602_CR29","doi-asserted-by":"crossref","unstructured":"Xie E, Ding J, Wang W, Zhan X, Xu H, Li Z, Luo P (2021) Detco: unsupervised contrastive learning for object detection. arXiv:2102.04803","DOI":"10.1109\/ICCV48922.2021.00828"},{"issue":"6","key":"602_CR30","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1109\/TAC.1979.1102177","volume":"24","author":"D Reid","year":"1979","unstructured":"Reid D (1979) An algorithm for tracking multiple targets. IEEE Trans Autom Control 24(6):843\u2013854","journal-title":"IEEE Trans Autom Control"},{"issue":"3","key":"602_CR31","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/JOE.1983.1145560","volume":"8","author":"T Fortmann","year":"1983","unstructured":"Fortmann T, Bar-Shalom Y, Scheffe M (1983) Sonar tracking of multiple targets using joint probabilistic data association. IEEE J Oceanic Eng 8(3):173\u2013184","journal-title":"IEEE J Oceanic Eng"},{"key":"602_CR32","doi-asserted-by":"crossref","unstructured":"Bewley A, Ge Z, Ott L, Ramos F, Upcroft B (2016) Simple online and realtime tracking. In: 2016 IEEE international conference on image processing (ICIP), IEEE, pp 3464\u20133468","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"602_CR33","doi-asserted-by":"crossref","unstructured":"Wojke N, Bewley A, Paulus D (2017) Simple online and realtime tracking with a deep association metric. In: 2017 IEEE international conference on image processing (ICIP), IEEE, pp 3645\u20133649","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"602_CR34","doi-asserted-by":"crossref","unstructured":"Chen L, Ai H, Zhuang Z, Shang C (2018) Real-time multiple people tracking with deeply learned candidate selection and person re-identification. In: 2018 IEEE international conference on multimedia and expo (ICME), IEEE, pp 1\u20136","DOI":"10.1109\/ICME.2018.8486597"},{"key":"602_CR35","doi-asserted-by":"crossref","unstructured":"Wang Z, Zheng L, Liu Y, Li Y, Wang S (2020) Towards real-time multi-object tracking. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XI 16, Springer, pp 107\u2013122","DOI":"10.1007\/978-3-030-58621-8_7"},{"key":"602_CR36","doi-asserted-by":"crossref","unstructured":"Zhou X, Koltun V, Kr\u00e4henb\u00fchl P (2020) Tracking objects as points. In: European conference on computer vision, Springer, pp 474\u2013490","DOI":"10.1007\/978-3-030-58548-8_28"},{"key":"602_CR37","doi-asserted-by":"crossref","unstructured":"Wu J, Cao J, Song L, Wang Y, Yang M, Yuan J (2021) Track to detect and segment: an online multi-object tracker. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12352\u201312361","DOI":"10.1109\/CVPR46437.2021.01217"},{"key":"602_CR38","doi-asserted-by":"crossref","unstructured":"Shuai B, Berneshawi A, Li X, Modolo D, Tighe J (2021) Siammot: Siamese multi-object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12372\u201312382","DOI":"10.1109\/CVPR46437.2021.01219"},{"issue":"4","key":"602_CR39","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1007\/s40747-020-00161-4","volume":"7","author":"S Liu","year":"2021","unstructured":"Liu S, Liu D, Srivastava G, Po\u0142ap D, Wo\u017aniak M (2021) Overview and methods of correlation filter algorithms in object tracking. Compl Intell Syst 7(4):1895\u20131917","journal-title":"Compl Intell Syst"},{"key":"602_CR40","unstructured":"Lucas BD, Kanade T et\u00a0al (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of the seventh international joint conference on artificial intelligence, Vancouver, British Columbia, pp 674\u2013679"},{"key":"602_CR41","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision, Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"602_CR42","unstructured":"Milan A, Leal-Taix\u00e9 L, Reid I, Roth S, Schindler K (2016) Mot16: a benchmark for multi-object tracking. arXiv:1603.00831"},{"key":"602_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2008\/246309","volume":"2008","author":"K Bernardin","year":"2008","unstructured":"Bernardin K, Stiefelhagen R (2008) Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J Image Video Process 2008:1\u201310","journal-title":"EURASIP J Image Video Process"},{"key":"602_CR44","doi-asserted-by":"crossref","unstructured":"Li Y, Huang C, Nevatia R (2009) Learning to associate: Hybridboosted multi-target tracker for crowded scene. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 2953\u20132960","DOI":"10.1109\/CVPR.2009.5206735"},{"key":"602_CR45","doi-asserted-by":"crossref","unstructured":"Chan AB, Vasconcelos N (2005) Classification and retrieval of traffic video using auto-regressive stochastic processes. In: IEEE proceedings intelligent vehicles symposium, IEEE, pp 771\u2013776","DOI":"10.1109\/IVS.2005.1505198"},{"key":"602_CR46","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.neucom.2004.12.001","volume":"64","author":"D Srinivasan","year":"2005","unstructured":"Srinivasan D, Jin X, Cheu RL (2005) Adaptive neural network models for automatic incident detection on freeways. Neurocomputing 64:473\u2013496","journal-title":"Neurocomputing"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00602-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-021-00602-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00602-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T17:08:48Z","timestamp":1651252128000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-021-00602-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,19]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["602"],"URL":"https:\/\/doi.org\/10.1007\/s40747-021-00602-8","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,19]]},"assertion":[{"value":"15 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there do not have any conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}