{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:40:36Z","timestamp":1782319236547,"version":"3.54.5"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100006283","name":"Federal Railroad Administration","doi-asserted-by":"publisher","award":["693JJ620C000021"],"award-info":[{"award-number":["693JJ620C000021"]}],"id":[{"id":"10.13039\/100006283","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00521-022-07660-0","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T09:04:34Z","timestamp":1660295074000},"page":"22099-22113","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A deep learning framework for detecting and localizing abnormal pedestrian behaviors at grade crossings"],"prefix":"10.1007","volume":"34","author":[{"given":"Zhuocheng","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5750-3181","authenticated-orcid":false,"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,12]]},"reference":[{"key":"7660_CR1","unstructured":"FRA (2019) Highway-rail crossing handbook - Third Edition. https:\/\/safety.fhwa.dot.gov\/hsip\/xings\/com_roaduser\/fhwasa18040\/fhwasa18040v2.pdf"},{"key":"7660_CR2","unstructured":"FRA (2018) National strategy to prevent trespassing on railroad property. https:\/\/www.fra.dot.gov\/eLib\/Details\/L19817"},{"key":"7660_CR3","unstructured":"FRA (2021) Highway\/rail grade crossing incidents. https:\/\/railroads.dot.gov\/accident-and-incident-reporting\/highwayrail-grade-crossing-incidents\/highwayrail-grade-crossing"},{"issue":"2","key":"7660_CR4","first-page":"1","volume":"54","author":"G Pang","year":"2020","unstructured":"Pang G, Shen C, Cao L, Hengel A (2020) Deep learning for anomaly detection: a review. ACM Comput Surv Mar 54(2):1\u201338","journal-title":"ACM Comput Surv Mar"},{"issue":"10","key":"7660_CR5","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1109\/TIFS.2019.2900907","volume":"14","author":"JT Zhou","year":"2019","unstructured":"Zhou JT, Du J, Zhu H, Peng X, Liu Y, Goh R (2019) Anomalynet: an anomaly detection network for video surveillance. IEEE Trans Inf Forensics Secur Oct 14(10):2537\u20132550","journal-title":"IEEE Trans Inf Forensics Secur Oct"},{"key":"7660_CR6","volume-title":"Transfer representation-learning for anomaly detection","author":"J Andrews","year":"2016","unstructured":"Andrews J, Tanay T, Morton EJ, Griffin LD (2016) Transfer representation-learning for anomaly detection. Proc. Int. Conf., Machine learning (ICML), July, New York, NY"},{"key":"7660_CR7","doi-asserted-by":"crossref","unstructured":"Ionescu RT, Khan FS, Georgescu M, Shao L(2019) Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 7834\u20137843","DOI":"10.1109\/CVPR.2019.00803"},{"key":"7660_CR8","doi-asserted-by":"crossref","unstructured":"Yu W, Cheng W, Aggarwal CC, Zhang K, Chen H, Wang W (2018) Netwalk: a flexible deep embedding approach for anomaly detection in dynamic networks. In: Proceedings of 24th ACM SIGKDD International conference on Knowledge Discovery and Data Mining (KDD), London, UK , 2672\u20132681","DOI":"10.1145\/3219819.3220024"},{"key":"7660_CR9","doi-asserted-by":"crossref","unstructured":"Sabokrou M, Khalooei M, Fathy M, Adeli E(2018) Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Salt Lake City, Utah, USA, 3379\u20133389 June 2018","DOI":"10.1109\/CVPR.2018.00356"},{"key":"7660_CR10","doi-asserted-by":"crossref","unstructured":"Nguyen T, Meunier J (2019) Anomaly Detection in Video Sequence with Appearance-Motion Correspondence. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), Seoul, Korea, 1273\u20131283, Oct. 2019","DOI":"10.1109\/ICCV.2019.00136"},{"key":"7660_CR11","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, .\u00a0Chen Y, Feng X, Lumezanu C, Cheng W, Ni J, Zong B, Chen H, Chawla NV (2019) A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: Proceedings of the AAAI conference on artificial intelligence, Jan., Honolulu, Hawaii, USA","DOI":"10.1609\/aaai.v33i01.33011409"},{"issue":"10262","key":"7660_CR12","first-page":"189","volume":"2017","author":"YS Chong","year":"2017","unstructured":"Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. Adv Neural Netw - ISNN 2017(10262):189\u2013196","journal-title":"Adv Neural Netw - ISNN"},{"key":"7660_CR13","doi-asserted-by":"crossref","unstructured":"Zhao Y, Deng B, Shen C, Liu Y, Lu H, Hua X (2017) Spatio-temporal autoencoder for video anomaly detection. In: Proceedings of the 25th ACM international conference on Multimedia, Mountain View, CA, USA , 1933\u20131941","DOI":"10.1145\/3123266.3123451"},{"key":"7660_CR14","unstructured":"Malhotra P, Ramakrishnan A, Anand G, Vig L, Agarwal P, Shroff G (2016) LSTM-based encoder-decoder for multi-sensor anomaly detection. International conference of machine learning (ICML) Anomaly detection Workshop, New York, NY"},{"key":"7660_CR15","doi-asserted-by":"crossref","unstructured":"Liu W, Luo W, Lian D, Gao S(2018) Future frame prediction for anomaly detection - a new baseline. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Salt Lake City, Utah, 6536\u20136545","DOI":"10.1109\/CVPR.2018.00684"},{"key":"7660_CR16","doi-asserted-by":"crossref","unstructured":"Pang G, Yan C, Shen C, Hengel A, Bai X(2020) Self-trained deep ordinal regression for end-to-end video anomaly detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), 12173\u201312182","DOI":"10.1109\/CVPR42600.2020.01219"},{"key":"7660_CR17","doi-asserted-by":"crossref","unstructured":"Park H, Noh J, Ham B(2020) Learning memory-guided normality for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14372\u201314381","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"7660_CR18","doi-asserted-by":"crossref","unstructured":".\u00a0Gupta A, Johnson J, Li F, Savarese S, .\u00a0Alahi A(2018) Social GAN: socially acceptable trajectories with generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Salt Lake City, Utah, 2255\u20132264","DOI":"10.1109\/CVPR.2018.00240"},{"issue":"7","key":"7660_CR19","doi-asserted-by":"publisher","first-page":"3010","DOI":"10.1109\/TIP.2016.2552404","volume":"25","author":"Y Du","year":"2016","unstructured":"Du Y, Fu Y, Wang L (2016) Representation learning of temporal dynamics for skeleton-based action recognition. IEEE Trans Image Process Oct 25(7):3010\u20133022","journal-title":"IEEE Trans Image Process Oct"},{"key":"7660_CR20","doi-asserted-by":"crossref","unstructured":"Fragkiadaki K, Levine S, Felsen P, Malik J(2015) Recurrent network models for human dynamics. In: Proceedings of the IEEE International conference on computer vision (ICCV), Santiago, Chile, 4346\u20134354","DOI":"10.1109\/ICCV.2015.494"},{"key":"7660_CR21","unstructured":"Villegas R, Yang J, Zou Y, Sohn S, Lin X, Lee H(2017) Learning to generate long-term future via hierarchical prediction. International conference on machine Learning (ICML), Sydney, Australia, 3560\u20133569"},{"key":"7660_CR22","unstructured":"Bera A, Manocha D(2018) Interactive surveillance technologies for dense crowds. In: Proceedings of the Association for the Advances of Artificial Intelligence. (AAAI), Arlington, Virginia, USA"},{"key":"7660_CR23","doi-asserted-by":"crossref","unstructured":"Piergiovanni A, Ryoo MS(2019) Representation flow for action recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), Long Beach, CA 9945\u20139953","DOI":"10.1109\/CVPR.2019.01018"},{"key":"7660_CR24","doi-asserted-by":"crossref","unstructured":"Morais R, Le V, Tran T, Saha B, Mansour M, Venkatesh S(2019) Learning regularity in skeleton trajectories for anomaly detection in videos. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition(CVPR), Long Beach, CA, 11996\u201312004","DOI":"10.1109\/CVPR.2019.01227"},{"key":"7660_CR25","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1007\/978-3-319-11752-2_56","volume":"8753","author":"L Pishchulin","year":"2014","unstructured":"Pishchulin L, Andriluka M, Schiele B (2014) Fine-grained activity recognition with holistic and pose based features. Pattern Recogn 8753:678\u2013689","journal-title":"Pattern Recogn"},{"key":"7660_CR26","doi-asserted-by":"crossref","unstructured":"Su C, Li J, Zhang S, Xing J, Gao W, Tian Q(2017) Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE international conference on computer vision (ICCV), Venice, Italy, 3980\u20133989 Oct 2017","DOI":"10.1109\/ICCV.2017.427"},{"key":"7660_CR27","doi-asserted-by":"crossref","unstructured":"Fang H, .\u00a0Lu G, Fang X, Xie J, Tai Y, Lu C(2018) Weakly and semi supervised human body part parsing via pose-guided knowledge transfer. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA 70\u201378, June 2018","DOI":"10.1109\/CVPR.2018.00015"},{"key":"7660_CR28","doi-asserted-by":"crossref","unstructured":"Li J, Wang C, Zhu H, Mao Y, Fang H, Lu C(2019) CrowdPose: efficient crowded scenes pose estimation and a new benchmark. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), Long Beach, CA, USA, 10855\u201310864, June 2019","DOI":"10.1109\/CVPR.2019.01112"},{"key":"7660_CR29","doi-asserted-by":"crossref","unstructured":"Fang H, Xie S, Tai Y, Lu C(2017) RMPE: regional multi-person pose estimation. InProceedings of the IEEE international conference on computer vision (ICCV), Venice, Italy, 4321\u20134331 Oct 2017","DOI":"10.1109\/ICCV.2017.256"},{"key":"7660_CR30","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei S, Sheikh Y(2017) Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, 7291\u20137299 July 2017","DOI":"10.1109\/CVPR.2017.143"},{"key":"7660_CR31","unstructured":"Xiu Y, Li J, Wang H, Fang Y, Lu C (2018) Pose flow: efficient online pose tracking. In: Proceedings of British Machine Vision Conference (BMVC), Newcastle, UK, Sep. 2018"},{"key":"7660_CR32","doi-asserted-by":"crossref","unstructured":"Simon T, Joo H, Matthews I, Sheikh Y (2017) Hand keypoint detection in single images using multiview bootstrapping. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 1145\u20131153 July 2017","DOI":"10.1109\/CVPR.2017.494"},{"key":"7660_CR33","unstructured":"Gao Y, Glowacka D (2016) Deep gate recurrent neural network. In: Proceedings of The 8th Asian Conference on Machine Learning, Hamilton, New Zealand, 350\u2013365 Nov. 2016"},{"key":"7660_CR34","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.ssci.2017.11.023","volume":"110","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Trivedi C, Liu X (2018) Automated detection of grade-crossing-trespassing near misses based on computer vision analysis of surveillance video data. Saf Sci Dec 110:276\u2013285","journal-title":"Saf Sci Dec"},{"key":"7660_CR35","doi-asserted-by":"crossref","unstructured":"Zhang J, Yang K, Rainer S (2021) ISSAFE: improving semantic segmentation in accidents by fusing event-based data. IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). 1132\u20131139 Oct. 2021","DOI":"10.1109\/IROS51168.2021.9636109"},{"key":"7660_CR36","doi-asserted-by":"publisher","first-page":"4715","DOI":"10.1007\/s00521-021-06625-z","volume":"34","author":"Z Jiang","year":"2022","unstructured":"Jiang Z, Guo F, Qian Y, Wang Y (2022) A deep learning-assisted mathematical model for decongestion time prediction at railroad grade crossings. Neural Comput Appl Oct 34:4715\u20134732","journal-title":"Neural Comput Appl Oct"},{"key":"7660_CR37","doi-asserted-by":"crossref","unstructured":"Zhao Y, Wu W, He Y, Li Y, Tan X, Chen S(2021) Good practices and a strong baseline for traffic anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, 3993\u20134001 June 2021","DOI":"10.1109\/CVPRW53098.2021.00450"},{"key":"7660_CR38","doi-asserted-by":"crossref","unstructured":"Doshi K, Yilmaz Y(2020) Fast unsupervised anomaly detection in traffic videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPR), Virtual, 624\u2013625 June 2020","DOI":"10.1109\/CVPRW50498.2020.00320"},{"key":"7660_CR39","doi-asserted-by":"crossref","unstructured":"Gasparini R, Pini S, Borghi G, Scaglione G, .\u00a0Calderara S, Fedeli E, Cucchiara R (2020) Anomaly detection for vision-based railway inspection. In: Proceedings of European Dependable Computing Conference, Munich, Germany, Sep. 2020","DOI":"10.1007\/978-3-030-58462-7_5"},{"key":"7660_CR40","unstructured":"UCSD Anomaly detection dataset. http:\/\/www.svcl.ucsd.edu\/projects\/anomaly\/dataset.html"},{"key":"7660_CR41","unstructured":"ShanghaiTech campus dataset (Anomaly detection). https:\/\/svip-lab.github.io\/dataset\/campus_dataset.html"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07660-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07660-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07660-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T12:33:41Z","timestamp":1676378021000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07660-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,12]]},"references-count":41,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["7660"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07660-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,12]]},"assertion":[{"value":"7 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Delcarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}