{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:07:29Z","timestamp":1776276449218,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningbo Science and Technology Innovation Project","award":["2020Z013"],"award-info":[{"award-number":["2020Z013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In order to solve the problem of long video dependence and the difficulty of fine-grained feature extraction in the video behavior recognition of personnel sleeping at a security-monitored scene, this paper proposes a time-series convolution-network-based sleeping behavior recognition algorithm suitable for monitoring data. ResNet50 is selected as the backbone network, and the self-attention coding layer is used to extract rich contextual semantic information; then, a segment-level feature fusion module is constructed to enhance the effective transmission of important information in the segment feature sequence on the network, and the long-term memory network is used to model the entire video in the time dimension to improve behavior detection ability. This paper constructs a data set of sleeping behavior under security monitoring, and the two behaviors contain about 2800 single-person target videos. The experimental results show that the detection accuracy of the network model in this paper is significantly improved on the sleeping post data set, up to 6.69% higher than the benchmark network. Compared with other network models, the performance of the algorithm in this paper has improved to different degrees and has good application value.<\/jats:p>","DOI":"10.3390\/jimaging9030060","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T03:11:39Z","timestamp":1678158699000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Sleep Action Recognition Based on Segmentation Strategy"],"prefix":"10.3390","volume":"9","author":[{"given":"Xiang","family":"Zhou","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China"}]},{"given":"Yue","family":"Cui","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, Advanced Manufacturing Institute, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315211, China"}]},{"given":"Gang","family":"Xu","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, Advanced Manufacturing Institute, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315211, China"}]},{"given":"Hongliang","family":"Chen","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, Advanced Manufacturing Institute, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315211, China"}]},{"given":"Jing","family":"Zeng","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China"}]},{"given":"Yutong","family":"Li","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, Advanced Manufacturing Institute, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315211, China"}]},{"given":"Jiangjian","family":"Xiao","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, Advanced Manufacturing Institute, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315211, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"ref_1","first-page":"7","article-title":"An overview of abnormal behavior detection algorithms in intelligent video surveillance systems","volume":"29","author":"Zeng","year":"2021","journal-title":"Comput. Meas. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-018-3692-x","article-title":"Video crowd detection and abnormal behavior model detection based on machine learning method","volume":"31","author":"Xie","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.neucom.2018.02.012","article-title":"Anomaly detection based on Nearest Neighbor search with Locality-Sensitive B-tree","volume":"289","author":"Shen","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s13634-018-0574-4","article-title":"Abnormal event detection in crowded scenes using histogram of oriented contextual gradient descriptor","volume":"2018","author":"Hu","year":"2018","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1109\/TMM.2018.2818942","article-title":"Anomaly Detection Based on Stacked Sparse Coding with Intraframe Classification Strategy","volume":"20","author":"Xu","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.cviu.2018.02.006","article-title":"Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes","volume":"172","author":"Sabokrou","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/TCSVT.2016.2589859","article-title":"Toward Abnormal Trajectory and Event Detection in Video Surveillance","volume":"27","author":"Cosar","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97564","DOI":"10.1109\/ACCESS.2020.2997357","article-title":"Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes","volume":"8","author":"Ye","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mehran, R., Oyama, A., and Shah, M. (2009, January 20\u201325). Abnormal crowd behavior detection using social force model. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.neunet.2018.09.002","article-title":"Soft + Hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection","volume":"108","author":"Fernando","year":"2018","journal-title":"Neural Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9692","DOI":"10.1109\/TIE.2018.2881943","article-title":"Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM","volume":"66","author":"Ullah","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TSMC.2013.2279661","article-title":"Camera Selection for Adaptive Human-Computer Interface","volume":"44","author":"Martinel","year":"2014","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Fathy, M., Hosseini, M., and Klette, R. (2015, January 11\u201312). Real-time anomaly detection and localization in crowded scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301284"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, H., and Schmid, C. (2013, January 1\u20138). Action Recognition with Improved Trajectories. Proceedings of the ICCV\u2014IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.441"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D Convolutional Neural Networks for Human Action Recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/TPAMI.2016.2599174","article-title":"Long-Term Recurrent Convolutional Networks for Visual Recognition and Description","volume":"39","author":"Donahue","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","article-title":"Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features","volume":"6","author":"Ullah","year":"2018","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gammulle, H., Denman, S., Sridharan, S., and Fookes, C. (2017, January 24\u201331). Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.27"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, Q., Qiu, Z., Yao, T., Tao, M., and Luo, J. (2016, January 6\u20139). Action Recognition by Learning Deep Multi-Granular Spatio-Temporal Video Representation. Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, New York, NY, USA.","DOI":"10.1145\/2911996.2912001"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.cviu.2017.10.011","article-title":"VideoLSTM convolves, attends and flows for action recognition","volume":"166","author":"Li","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.image.2018.09.003","article-title":"TS-LSTM and temporal-inception: Exploiting spatiotemporal dynamics for activity recognition","volume":"71","author":"Ma","year":"2019","journal-title":"Signal Process. Image Commun."},{"key":"ref_23","first-page":"568","article-title":"Two-Stream Convolutional Networks for Action Recognition in Videos","volume":"27","author":"Simonyan","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","unstructured":"Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., and Gool, L.V. (2016). European Conference on Computer Vision, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Pinz, A., and Wildes, R.P.J.I. (2017, January 21\u201326). Spatiotemporal Residual Networks for Video Action Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.787"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., and He, K. (November, January 27). SlowFast Networks for Video Recognition. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00630"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.clinph.2022.08.022","article-title":"Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels","volume":"143","author":"Moghadam","year":"2022","journal-title":"Clin. Neurophysiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"S129","DOI":"10.1016\/j.sleep.2022.05.353","article-title":"Leveraging machine learning to identify the neural correlates of insomnia with and without sleep state misperception","volume":"100","author":"Andrillon","year":"2022","journal-title":"J. Sleep Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109421","DOI":"10.1016\/j.jneumeth.2021.109421","article-title":"Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning","volume":"366","author":"Zhang","year":"2022","journal-title":"J. Neurosci. Methods"},{"key":"ref_30","unstructured":"Yan, X., Lv, W., and Hua, W. (2018). Statistical analysis of college students\u2019 sleeping behavior in class based on video data. Ind. Control Comput., 31, 122-123+126."},{"key":"ref_31","unstructured":"Shuwei, Z. (2021). Research and Application of Human Behavior Recognition Algorithm for Intelligent Security Scene. [Master\u2019s Thesis, Xi\u2019an University of Electronic Technology]."},{"key":"ref_32","first-page":"5998","article-title":"Attention is All you Need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tan, Z., Wang, M., Xie, J., Chen, Y., and Shi, X.J.A. (2018, January 2\u20137). Deep Semantic Role Labeling with Self-Attention. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11928"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/3\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:49:41Z","timestamp":1760122181000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/3\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,7]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["jimaging9030060"],"URL":"https:\/\/doi.org\/10.3390\/jimaging9030060","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,7]]}}}