{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:50:46Z","timestamp":1760147446138,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"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>High-speed detection of abnormal frames in surveillance videos is essential for security. This paper proposes a new video anomaly\u2013detection model, namely, feature trajectory\u2013smoothed long short-term memory (FTS-LSTM). This model trains an LSTM autoencoder network to generate future frames on normal video streams, and uses the FTS detector and generation error (GE) detector to detect anomalies on testing video streams. FTS loss is a new indicator in the anomaly\u2013detection area. In the training stage, the model applies a feature trajectory smoothness (FTS) loss to constrain the LSTM layer. This loss enables the LSTM layer to learn the temporal regularity of video streams more precisely. In the detection stage, the model utilizes the FTS loss and the GE loss as two detectors to detect anomalies. By cascading the FTS detector and the GE detector to detect anomalies, the model achieves a high speed and competitive anomaly-detection performance on multiple datasets.<\/jats:p>","DOI":"10.3390\/s23031612","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T01:53:54Z","timestamp":1675302834000},"page":"1612","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Feature-Trajectory-Smoothed High-Speed Model for Video Anomaly Detection"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2714-8526","authenticated-orcid":false,"given":"Li","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9867-2539","authenticated-orcid":false,"given":"Zhiguo","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2372-1180","authenticated-orcid":false,"given":"Yujin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2131-3044","authenticated-orcid":false,"given":"Guijin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"},{"name":"Shanghai AI Laboratory, Shanghai 200232, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1109\/LSP.2015.2410031","article-title":"Learning to detect anomalies in surveillance video","volume":"22","author":"Xiao","year":"2015","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_2","first-page":"1","article-title":"Anomaly detection","volume":"14","author":"Prasad","year":"2009","journal-title":"Comput. Mater. Contin."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, I., Jeon, Y., Kang, J.W., and Gwak, J. (2022). RAG-PaDiM: Residual Attention Guided PaDiM for Defects Segmentation in Railway Tracks. J. Electr. Eng. Technol.","DOI":"10.1007\/s42835-022-01346-2"},{"key":"ref_4","first-page":"1","article-title":"Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles","volume":"73","author":"Kang","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kang, J., Kim, C.S., Kang, J.W., and Gwak, J. (2021). Anomaly detection of the brake operating unit on metro vehicles using a one-class lstm autoencoder. Appl. Sci., 11.","DOI":"10.3390\/app11199290"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, T., Aftab, W., Mihaylova, L., Langran-Wheeler, C., Rigby, S., Fletcher, D., Maddock, S., and Bosworth, G. (2022). Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention\u2014A Survey. Sensors, 22.","DOI":"10.3390\/s22124324"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Khan, S.W., Hafeez, Q., Khalid, M.I., Alroobaea, R., Hussain, S., Iqbal, J., Almotiri, J., and Ullah, S.S. (2022). Anomaly Detection in Traffic Surveillance Videos Using Deep Learning. Sensors, 22.","DOI":"10.3390\/s22176563"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., and Gao, S. (2018, January 18\u201322). Future Frame Prediction for Anomaly Detection\u2014A New Baseline. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ullah, W., Ullah, A., Hussain, T., Khan, Z.A., and Baik, S.W. (2021). An efficient anomaly recognition framework using an attention residual lstm in surveillance videos. Sensors, 21.","DOI":"10.3390\/s21082811"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dubey, S., Boragule, A., Gwak, J., and Jeon, M. (2021). Anomalous event recognition in videos based on joint learning of motion and appearance with multiple ranking measures. Appl. Sci., 11.","DOI":"10.3390\/app11031344"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ionescu, R.T., Smeureanu, S., Alexe, B., and Popescu, M. (2017, January 22\u201329). Unmasking the Abnormal Events in Video. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.315"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LSP.2018.2889273","article-title":"One-Class Convolutional Neural Network","volume":"26","author":"Oza","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_13","first-page":"60","article-title":"One-class neural network for video anomaly detection and localization","volume":"35","author":"Weixiang","year":"2021","journal-title":"Electron. Meas. Instrum."},{"key":"ref_14","first-page":"2609","article-title":"A Deep One-Class Neural Network for Anomalous Event Detection in Complex Scenes","volume":"31","author":"Wu","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_15","first-page":"481","article-title":"Latent space autoregression for novelty detection","volume":"Volume 2019","author":"Abati","year":"2019","journal-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1109\/JAS.2021.1004045","article-title":"A Cognitive Memory-Augmented Network for Visual Anomaly Detection","volume":"8","author":"Wang","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_17","first-page":"488","article-title":"AVID: Adversarial Visual Irregularity Detection","volume":"Volume 11366 LNCS","author":"Sabokrou","year":"2018","journal-title":"Computer Vision\u2014ACCV 2018, Proceedings of the 14th Asian Conference on Computer Vision, Perth, Australia, 2\u20136 December 2018"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/TMM.2019.2950530","article-title":"Learning Normal Patterns via Adversarial Attention-Based Autoencoder for Abnormal Event Detection in Videos","volume":"22","author":"Song","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1109\/TPAMI.2019.2944377","article-title":"Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks","volume":"43","author":"Luo","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.patrec.2019.11.024","article-title":"Integrating prediction and reconstruction for anomaly detection","volume":"129","author":"Tang","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, S., Kim, H.G., and Ro, Y.M. (2018, January 15\u201320). STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462388"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ji, X., Li, B., and Zhu, Y. (2020, January 19\u201324). TAM-Net: Temporal Enhanced Appearance-to-Motion Generative Network for Video Anomaly Detection. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207231"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1109\/LSP.2021.3107750","article-title":"Main-Auxiliary Aggregation Strategy for Video Anomaly Detection","volume":"28","author":"Wang","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/978-3-319-59081-3_23","article-title":"Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder","volume":"Volume 10262","author":"Chong","year":"2017","journal-title":"Advances in Neural Networks\u2014ISNN 2017"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., and Gao, S. (2017, January 10\u201314). Remembering history with convolutional LSTM for anomaly detection. Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019325"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., and Davis, L.S. (2016, January 27\u201330). Learning Temporal Regularity in Video Sequences. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.86"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Huang, C., Wen, J., Xu, Y., Jiang, Q., Yang, J., Wang, Y., and Zhang, D. (2022). Self-Supervised Attentive Generative Adversarial Networks for Video Anomaly Detection. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315.","DOI":"10.1109\/TNNLS.2022.3159538"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ionescu, R.T., Smeureanu, S., Popescu, M., and Alexe, B. (2019, January 7\u201311). Detecting Abnormal Events in Video Using Narrowed Normality Clusters. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA.","DOI":"10.1109\/WACV.2019.00212"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hinami, R., Mei, T., and Satoh, S. (2017, January 22\u201329). Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.391"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/TIP.2008.919359","article-title":"Infinite Hidden Markov Models for Unusual-Event Detection in Video","volume":"17","author":"Carin","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.cviu.2007.06.004","article-title":"Incremental and adaptive abnormal behaviour detection","volume":"111","author":"Xiang","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1109\/TIFS.2018.2868617","article-title":"Squirrel-cage local binary pattern and its application in video anomaly detection","volume":"14","author":"Hu","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/978-3-030-20005-3_9","article-title":"Video Anomaly Detection and Localization in Crowded Scenes","volume":"951","author":"Gnouma","year":"2020","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lu, C., Shi, J., and Jia, J. (2013, January 1\u20138). Abnormal Event Detection at 150 FPS in MATLAB. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.338"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cong, Y., Yuan, J., and Liu, J. (2011, January 20\u201325). Sparse reconstruction cost for abnormal event detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995434"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/TMM.2018.2846411","article-title":"Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos","volume":"21","author":"Chu","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102920","DOI":"10.1016\/j.cviu.2020.102920","article-title":"Video anomaly detection and localization via Gaussian Mixture Fully Convolutional Variational Autoencoder","volume":"195","author":"Fan","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Khalooei, M., Fathy, M., and Adeli, E. (2018, January 18\u201322). Adversarially Learned One-Class Classifier for Novelty Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00356"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh, M., Nabi, M., Sangineto, E., Marcenaro, L., Regazzoni, C., and Sebe, N. (2017, January 17\u201320). Abnormal event detection in videos using generative adversarial nets. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296547"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., and Van Den Hengel, A. (November, January 27). Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00179"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4639","DOI":"10.1109\/TCSVT.2019.2962229","article-title":"Attention-Driven Loss for Anomaly Detection in Video Surveillance","volume":"30","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_42","unstructured":"Medel, J.R., and Savakis, A. (2016). Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lu, Y., Kumar, K.M., Nabavi, S.S., and Wang, Y. (2019, January 18\u201321). Future Frame Prediction Using Convolutional VRNN for Anomaly Detection. Proceedings of the 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Taipei, Taiwan.","DOI":"10.1109\/AVSS.2019.8909850"},{"key":"ref_44","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML\u201910), Madison, WI, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.patrec.2020.09.019","article-title":"A promotion method for generation error-based video anomaly detection","volume":"140","author":"Wang","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mahadevan, V., Li, W., Bhalodia, V., and Vasconcelos, N. (2010, January 13\u201318). Anomaly detection in crowded scenes. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"ref_47","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_48","first-page":"447","article-title":"Image and Graphics","volume":"Volume 10666","author":"Wang","year":"2017","journal-title":"Ts-Unet: A Temporal Smoothed Unet for Video Anomaly Detection, Proceedings of the 11th International Conference on Image and Graphics, Shanghai, China, 13\u201315 September 2017"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1612\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:21:38Z","timestamp":1760120498000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1612"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,2]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031612"],"URL":"https:\/\/doi.org\/10.3390\/s23031612","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,2,2]]}}}