{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:01:01Z","timestamp":1780081261361,"version":"3.54.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"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"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-06066-w","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T02:37:34Z","timestamp":1732070254000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Highway spillage detection using an improved STPM anomaly detection network from a surveillance perspective"],"prefix":"10.1007","volume":"55","author":[{"given":"Haoxiang","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huansheng","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8898-4308","authenticated-orcid":false,"given":"Yongfeng","family":"Bu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"key":"6066_CR1","doi-asserted-by":"crossref","unstructured":"Liang H, Song H, Yun X et\u00a0al (2022) Traffic incident detection based on a global trajectory spatiotemporal map. Complex & Intelligent Systems pp 1\u201320","DOI":"10.1007\/s40747-021-00602-8"},{"key":"6066_CR2","doi-asserted-by":"publisher","first-page":"122813","DOI":"10.1016\/j.eswa.2023.122813","volume":"244","author":"P Moriano","year":"2024","unstructured":"Moriano P, Berres A, Xu H et al (2024) Spatiotemporal features of traffic help reduce automatic accident detection time. Expert Syst Appl 244:122813","journal-title":"Expert Syst Appl"},{"key":"6066_CR3","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.aap.2018.01.024","volume":"130","author":"J Wang","year":"2019","unstructured":"Wang J, Liu B, Fu T et al (2019) Modeling when and where a secondary accident occurs. Accid Anal Prev 130:160\u2013166","journal-title":"Accid Anal Prev"},{"key":"6066_CR4","doi-asserted-by":"publisher","first-page":"106019","DOI":"10.1016\/j.aap.2021.106019","volume":"154","author":"A Pramanik","year":"2021","unstructured":"Pramanik A, Sarkar S, Maiti J (2021) A real-time video surveillance system for traffic pre-events detection. Accid Anal Prev 154:106019","journal-title":"Accid Anal Prev"},{"key":"6066_CR5","doi-asserted-by":"publisher","first-page":"104078","DOI":"10.1016\/j.imavis.2020.104078","volume":"106","author":"R Nayak","year":"2021","unstructured":"Nayak R, Pati UC, Das SK (2021) A comprehensive review on deep learning-based methods for video anomaly detection. Image Vis Comput 106:104078","journal-title":"Image Vis Comput"},{"issue":"2","key":"6066_CR6","doi-asserted-by":"publisher","first-page":"1831","DOI":"10.1007\/s10489-024-05283-7","volume":"54","author":"S Yan","year":"2024","unstructured":"Yan S, Chen P, Chen H et al (2024) Multiresolution feature guidance based transformer for anomaly detection. Appl Intell 54(2):1831\u20131846","journal-title":"Appl Intell"},{"issue":"3","key":"6066_CR7","doi-asserted-by":"publisher","first-page":"467","DOI":"10.3233\/SW-200393","volume":"12","author":"AS Patel","year":"2021","unstructured":"Patel AS, Merlino G, Bruneo D et al (2021) Video representation and suspicious event detection using semantic technologies. Semantic Web 12(3):467\u2013491","journal-title":"Semantic Web"},{"issue":"4","key":"6066_CR8","doi-asserted-by":"publisher","first-page":"251","DOI":"10.36548\/jtcsst.2021.4.001","volume":"3","author":"A Sathesh","year":"2021","unstructured":"Sathesh A, Hamdan YB (2021) Speedy detection module for abandoned belongings in airport using improved image processing technique. J Trends Comput Sci Smart Technol 3(4):251","journal-title":"J Trends Comput Sci Smart Technol"},{"key":"6066_CR9","doi-asserted-by":"crossref","unstructured":"Din M, Bashir A, Basit A et al (2020) Abandoned object detection using frame differencing and background subtraction. Int J Adv Comput Sci Appl 11(7)","DOI":"10.14569\/IJACSA.2020.0110781"},{"issue":"2","key":"6066_CR10","first-page":"771","volume":"14","author":"H Park","year":"2020","unstructured":"Park H, Park S, Joo Y (2020) Robust real-time detection of abandoned objects using a dual background model. KSII Trans Internet Inf Syst (TIIS) 14(2):771\u2013788","journal-title":"KSII Trans Internet Inf Syst (TIIS)"},{"issue":"19","key":"6066_CR11","doi-asserted-by":"publisher","first-page":"29477","DOI":"10.1007\/s11042-023-14632-6","volume":"82","author":"H Su","year":"2023","unstructured":"Su H, Wang W, Wang S (2023) A robust all-weather abandoned objects detection algorithm based on dual background and gradient operator. Multimed Tools Appl 82(19):29477\u201329499","journal-title":"Multimed Tools Appl"},{"key":"6066_CR12","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.patrec.2019.11.004","volume":"129","author":"RR Boukhriss","year":"2020","unstructured":"Boukhriss RR, Fendri E, Hammami M (2020) Moving object detection under different weather conditions using full-spectrum light sources. Pattern Recogn Lett 129:205\u2013212","journal-title":"Pattern Recogn Lett"},{"key":"6066_CR13","doi-asserted-by":"crossref","unstructured":"Russel NS, Selvaraj A (2023) Ownership of abandoned object detection by integrating carried object recognition and context sensing. The Visual Computer pp 1\u201326","DOI":"10.1007\/s00371-023-03089-1"},{"key":"6066_CR14","doi-asserted-by":"crossref","unstructured":"An Y, Zhao X, Yu T, et\u00a0al (2023) Zbs: Zero-shot background subtraction via instance-level background modeling and foreground selection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6355\u20136364","DOI":"10.1109\/CVPR52729.2023.00615"},{"key":"6066_CR15","doi-asserted-by":"crossref","unstructured":"Dwivedi N, Singh DK, Kushwaha DS (2020) An approach for unattended object detection through contour formation using background subtraction. Procedia Comput Sci 171:1979\u20131988","DOI":"10.1016\/j.procs.2020.04.212"},{"issue":"14","key":"6066_CR16","doi-asserted-by":"publisher","first-page":"21627","DOI":"10.1007\/s11042-023-14696-4","volume":"82","author":"Y Teja","year":"2023","unstructured":"Teja Y (2023) Static object detection for video surveillance. Multimed Tools Appl 82(14):21627\u201321639","journal-title":"Multimed Tools Appl"},{"key":"6066_CR17","doi-asserted-by":"crossref","unstructured":"Ahammed MT, Ghosh S, Ashik MAR (2022) Human and object detection using machine learning algorithm. In: 2022 Trends in electrical, electronics, computer engineering conference (TEECCON), IEEE, pp 39\u201344","DOI":"10.1109\/TEECCON54414.2022.9854818"},{"key":"6066_CR18","doi-asserted-by":"crossref","unstructured":"Dogariu M, Stefan LD, Constantin MG et al (2020) Human-object interaction: application to abandoned luggage detection in video surveillance scenarios. In: 2020 13th International Conference on Communications (COMM), IEEE, pp 157\u2013160","DOI":"10.1109\/COMM48946.2020.9141973"},{"issue":"2","key":"6066_CR19","doi-asserted-by":"publisher","first-page":"188","DOI":"10.14445\/22315381\/IJETT-V69I2P226","volume":"69","author":"W Weliwita","year":"2021","unstructured":"Weliwita W, Isuru J, Premaratne S (2021) Modeling abandoned object detection and recognition in real-time surveillance. Int J Eng Trends Technol 69(2):188\u2013193","journal-title":"Int J Eng Trends Technol"},{"key":"6066_CR20","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhai J (2023) Highway abandoned object detection based on foreground extraction. In: Chinese Intelligent Systems Conference, Springer, pp 367\u2013376","DOI":"10.1007\/978-981-99-6847-3_31"},{"issue":"01","key":"6066_CR21","first-page":"1549","volume":"4","author":"SP Lwin","year":"2022","unstructured":"Lwin SP, Tun MT (2022) Deep convonlutional neural network for abandoned object detection. Int Res J Mod Eng Technol Sci 4(01):1549\u20131553","journal-title":"Int Res J Mod Eng Technol Sci"},{"key":"6066_CR22","doi-asserted-by":"publisher","first-page":"107944","DOI":"10.1016\/j.compeleceng.2022.107944","volume":"100","author":"F Li","year":"2022","unstructured":"Li F, Jiang Z, Zhou S et al (2022) Spilled load detection based on lightweight yolov4 trained with easily accessible synthetic dataset. Comput Electr Eng 100:107944","journal-title":"Comput Electr Eng"},{"key":"6066_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-021-01171-z","volume":"32","author":"S Zhou","year":"2021","unstructured":"Zhou S, Bi Y, Wei X et al (2021) Automated detection and classification of spilled loads on freeways based on improved yolo network. Mach Vis Appl 32:1\u201312","journal-title":"Mach Vis Appl"},{"key":"6066_CR24","doi-asserted-by":"crossref","unstructured":"Preetha K et al (2021) A fuzzy rule-based abandoned object detection using image fusion for intelligent video surveillance systems. Turk J Comput Math Educ (TURCOMAT) 12(3):3694\u20133702","DOI":"10.17762\/turcomat.v12i3.1652"},{"key":"6066_CR25","doi-asserted-by":"crossref","unstructured":"Lamar Leon J, Alonso Baryolo R, Garcia Reyes E et al (2023) Abandoned object detection using persistent homology. In: Iberoamerican congress on pattern recognition. Springer, pp 178\u2013188","DOI":"10.1007\/978-3-031-49018-7_13"},{"key":"6066_CR26","doi-asserted-by":"crossref","unstructured":"Asad M, Jiang H, Yang J et al (2022) Multi-stream 3d latent feature clustering for abnormality detection in videos. Appl Intell 52(1):1126\u20131143","DOI":"10.1007\/s10489-021-02356-9"},{"key":"6066_CR27","unstructured":"Huaiyu C, Zhaoqian Y, Ziyang C et al (2024) Image-guided and point cloud space-constrained method for detection and localization of abandoned objects on the road. Opto-Electron Eng 51(3):230317"},{"key":"6066_CR28","doi-asserted-by":"publisher","first-page":"3292","DOI":"10.1109\/TMM.2020.3023303","volume":"23","author":"C Sun","year":"2020","unstructured":"Sun C, Jia Y, Song H et al (2020) Adversarial 3d convolutional auto-encoder for abnormal event detection in videos. IEEE Trans Multimedia 23:3292\u20133305","journal-title":"IEEE Trans Multimedia"},{"issue":"19","key":"6066_CR29","doi-asserted-by":"publisher","first-page":"10011","DOI":"10.3390\/app121910011","volume":"12","author":"F Caetano","year":"2022","unstructured":"Caetano F, Carvalho P, Cardoso J (2022) Deep anomaly detection for in-vehicle monitoring\u2013an application-oriented review. Appl Sci 12(19):10011","journal-title":"Appl Sci"},{"key":"6066_CR30","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1109\/TIP.2019.2948286","volume":"29","author":"S Lee","year":"2019","unstructured":"Lee S, Kim HG, Ro YM (2019) Bman: Bidirectional multi-scale aggregation networks for abnormal event detection. IEEE Trans Image Process 29:2395\u20132408","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"6066_CR31","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.1109\/TNNLS.2021.3083152","volume":"33","author":"X Wang","year":"2021","unstructured":"Wang X, Che Z, Jiang B et al (2021) Robust unsupervised video anomaly detection by multipath frame prediction. IEEE Trans Neural Netw Learn Syst 33(6):2301\u20132312","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"6066_CR32","doi-asserted-by":"publisher","first-page":"3240","DOI":"10.1007\/s10489-022-03613-1","volume":"53","author":"VT Le","year":"2023","unstructured":"Le VT, Kim YG (2023) Attention-based residual autoencoder for video anomaly detection. Appl Intell 53(3):3240\u20133254","journal-title":"Appl Intell"},{"key":"6066_CR33","unstructured":"Cohen N, Hoshen Y (2020) Sub-image anomaly detection with deep pyramid correspondences. arXiv:2005.02357"},{"key":"6066_CR34","doi-asserted-by":"crossref","unstructured":"Li CL, Sohn K, Yoon J et al (2021) Cutpaste: Self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9664\u20139674","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"6066_CR35","doi-asserted-by":"crossref","unstructured":"Yi J, Yoon S (2020) Patch svdd: Patch-level svdd for anomaly detection and segmentation. In: Proceedings of the Asian conference on computer vision, pp 375\u2013390","DOI":"10.1007\/978-3-030-69544-6_23"},{"key":"6066_CR36","doi-asserted-by":"crossref","unstructured":"Bergmann P, Fauser M, Sattlegger D et al (2020) Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4183\u20134192","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"6066_CR37","unstructured":"Wang G, Han S, Ding E, et\u00a0al (2021) Student-teacher feature pyramid matching for anomaly detection. arXiv:2103.04257"},{"key":"6066_CR38","doi-asserted-by":"crossref","unstructured":"Bergmann P, Fauser M, Sattlegger D et al (2019) Mvtec ad\u2013a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9592\u20139600","DOI":"10.1109\/CVPR.2019.00982"},{"issue":"11","key":"6066_CR39","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1109\/34.888718","volume":"22","author":"Z Zhang","year":"2000","unstructured":"Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330\u20131334","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"6066_CR40","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang K, Zuo W, Zhang L (2018) Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. IEEE Trans Image Process 27(9):4608\u20134622","journal-title":"IEEE Trans Image Process"},{"key":"6066_CR41","doi-asserted-by":"crossref","unstructured":"Roth S, Black MJ (2005) Fields of experts: A framework for learning image priors. In: 2005 IEEE Computer society conference on computer vision and pattern recognition (CVPR\u201905), IEEE, pp 860\u2013867","DOI":"10.1109\/CVPR.2005.160"},{"key":"6066_CR42","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R et al (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. Ieee, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06066-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06066-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06066-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T06:11:55Z","timestamp":1735798315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06066-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,20]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6066"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06066-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,20]]},"assertion":[{"value":"4 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"7"}}