{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:45:12Z","timestamp":1764996312397,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T00:00:00Z","timestamp":1621382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China (International Scientific &amp; Technological Cooperation Program)","award":["2019YFE0106500"],"award-info":[{"award-number":["2019YFE0106500"]}]},{"name":"National Natural Science Foundation of China","award":["U1903214,62071339"],"award-info":[{"award-number":["U1903214,62071339"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Using personal trajectory information to grasp the spatiotemporal laws of dangerous activities to curb the occurrence of criminal acts is a new opportunity and method for security prevention and control. This paper proposes a novel method to discover abnormal behaviors and judge abnormal behavior patterns using mobility trajectory data. Abnormal behavior trajectory refers to the behavior trajectory whose temporal and spatial characteristics are different from normal behavior, and it is an important clue to discover dangerous behavior. Abnormal patterns are the behavior patterns summarized based on the regular characteristics of criminals\u2019 activities, including wandering, scouting, random walk, and trailing. This paper examines the abnormal behavior patterns based on mobility trajectories. A Long Short-Term Memory Network (LSTM)-based method is used to extract personal trajectory features, and the K-means clustering method is applied to extract abnormal trajectories from the trajectory dataset. Based on the characteristics of different abnormal behaviors, the spatio-temporal feature matching method is used to identify the abnormal patterns based on the filtered abnormal trajectories. Experimental results showed that the trajectory-based abnormal behavior discovery method can realize a rapid discovery of abnormal trajectories and effective judgment of abnormal behavior patterns.<\/jats:p>","DOI":"10.3390\/s21103520","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T23:34:46Z","timestamp":1621380886000},"page":"3520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Uncovering Abnormal Behavior Patterns from Mobility Trajectories"],"prefix":"10.3390","volume":"21","author":[{"given":"Hao","family":"Wu","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China"}]},{"given":"Xuehua","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"}]},{"given":"Zhongyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China"}]},{"given":"Nanxi","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.eswa.2017.09.029","article-title":"Abnormal behavior recognition for intelligent video surveillance systems: A review","volume":"91","author":"Zagrouba","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kulkarni, P., Patil, B., and Joglekar, B. (2015, January 28\u201330). An effective content based video analysis and retrieval using pattern indexing techniques. Proceedings of the 2015 International Conference on Industrial Instrumentation and Control (ICIC), Pune, India.","DOI":"10.1109\/IIC.2015.7150717"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.engappai.2018.08.014","article-title":"A review of state-of-the-art techniques for abnormal human activity recognition","volume":"77","author":"Dhiman","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","first-page":"2866","article-title":"Abnormal Scene Change Detection From a Moving Camera Using Bags of Patches and Spider-Web Map","volume":"15","author":"Hsieh","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.neucom.2015.11.021","article-title":"Crowd Behavior Analysis: A Review where Physics meets Biology","volume":"177","author":"Kok","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Selvaraj, H., Zydek, D., and Chmaj, G. (2015). Trajectory Based Unusual Human Movement Identification for Video Surveillance System. Progress in Systems Engineering, Springer International Publishing.","DOI":"10.1007\/978-3-319-08422-0"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pathak, D., Sharang, A., and Mukerjee, A. (2015, January 5\u20139). Anomaly Localization in Topic-Based Analysis of Surveillance Videos. Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, IEEE Computer Society, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.58"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yi, P., Wang, Z., Jiang, K., Jiang, J., Lu, T., and Ma, J. (2020). A Progressive Fusion Generative Adversarial Network for Realistic and Consistent Video Super-Resolution. Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2020.3042298"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"De Souza, F.D.M., Chavez, G.C., do Valle, E.A., and Araujo, A.D.A. (September, January 30). Violence Detection in Video Using Spatio-Temporal Features. Proceedings of the 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images, IEEE Computer Society, Gramado, Brazil.","DOI":"10.1109\/SIBGRAPI.2010.38"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hassner, T., Itcher, Y., and Kliper-Gross, O. (2012, January 16\u201321). Violent flows: Real-time detection of violent crowd behavior. Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6239348"},{"key":"ref_12","unstructured":"Martin, V., Glotin, H., Paris, S., Halkias, X., and Prevot, J.-M. (2012, January 4\u20135). Violence Detection in Video by Large Scale Multi-Scale Local Binary Patterns Dynamics. Proceedings of the CEUR Workshop Proceedings, Pisa, Italy."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"428","DOI":"10.15837\/ijccc.2015.3.1667","article-title":"Group Pattern Mining Algorithm of Moving Objects\u2019 Uncertain Trajectories","volume":"10","author":"Wang","year":"2015","journal-title":"Int. J. Comput. Commun. Control"},{"key":"ref_14","first-page":"380871.1","article-title":"Interesting Activities Discovery for Moving Objects Based on Collaborative Filtering","volume":"2013","author":"Guan","year":"2013","journal-title":"Math. Probl. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Park, J., Abdel-Aty, M., Wu, Y., and Mattei, I. (2018). Enhancing In-Vehicle Driving Assistance Information Under Connected Vehicle Environment. IEEE Trans. Intell. Transp. Syst., 3558\u20133567.","DOI":"10.1109\/TITS.2018.2878736"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, L., \u00d6zsu, M.T., and Oria, V. (2005). Robust and Fast Similarity Search for Moving Object Trajectories. Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery.","DOI":"10.1145\/1066157.1066213"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lee, J.G., Han, J., and Whang, K.Y. (2007). Trajectory Clustering: A Partition-and-Group Framework. Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery.","DOI":"10.1145\/1247480.1247546"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tang, W., Pi, D., and He, Y. (2016). A Density-Based Clustering Algorithm with Sampling for Travel Behavior Analysis. Intelligent Data Engineering and Automated Learning\u2014IDEAL 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46257-8_25"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, Z., Lee, J.G., Li, X., and Han, J. (2010). Incremental Clustering for Trajectories, Springer.","DOI":"10.1007\/978-3-642-12098-5_3"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3306","DOI":"10.1109\/TITS.2016.2547641","article-title":"Review and Perspective for Distance-Based Clustering of Vehicle Trajectories","volume":"17","author":"Besse","year":"2016","journal-title":"Trans. Intell. Transport. Sys."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Batista, E., Casino, F., and Solanas, A. (2016, January 13\u201315). On wandering detection methods in context-aware scenarios. Proceedings of the 2016 7th International Conference on Information, Intelligence, Systems Applications (IISA), Chalkidiki, Greece.","DOI":"10.1109\/IISA.2016.7785349"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Batista, E., Borras, F., Casino, F., and Solanas, A. (2015, January 6\u20138). A study on the detection of wandering patterns in human trajectories. Proceedings of the 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), Corfu, Greece.","DOI":"10.1109\/IISA.2015.7387995"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Ballest\u00e9, A., Budesca, F.B., and Solanas, A. (2015). An autonomous intelligent system for the private outdoors monitoring of people with mild cognitive impairments. Advanced Technological Solutions for E- Health and Dementia Patient Monitoring, IGI Global.","DOI":"10.4018\/978-1-4666-7481-3.ch006"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8328","DOI":"10.1038\/srep08328","article-title":"Precise positioning with current multi-constellation Global Navigation Satellite Systems: GPS, GLONASS, Galileo and BeiDou","volume":"5","author":"Li","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Vuong, N.K., Chan, S., Lau, C.T., and Lau, K.M. (2011). Feasibility Study of a Real-Time Wandering Detection Algorithm for Dementia Patients. Proceedings of the First ACM MobiHoc Workshop on Pervasive Wireless Healthcare, Association for Computing Machinery.","DOI":"10.1145\/2007036.2007050"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1093\/geront\/31.5.666","article-title":"Travel Behavior of Nursing Home Residents Perceived as Wanderers and Nonwanderers","volume":"31","author":"Blasch","year":"1991","journal-title":"Gerontologist"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chang, Y.J. (2010). Anomaly Detection for Travelling Individuals with Cognitive Impairments. SIGACCESS Access. Comput., 25\u201332.","DOI":"10.1145\/1873532.1873535"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yao, D., Zhang, C., Zhu, Z., Huang, J., and Bi, J. (2017, January 14\u201319). Trajectory clustering via deep representation learning. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966345"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012017","DOI":"10.1088\/1757-899X\/336\/1\/012017","article-title":"Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster","volume":"336","author":"Syakur","year":"2018","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_30","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. Sequence to Sequence Learning with Neural Networks. Proceedings of the 27th International Conference on Neural Information Processing Systems\u2014Volume 2."},{"key":"ref_31","first-page":"1","article-title":"Preparation for crime as a criminal attempt","volume":"1","author":"Strahorn","year":"1939","journal-title":"Wash. Lee L. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qi, M., Wang, Z., He, Z., and Shao, Z. (2019). User Identification across Asynchronous Mobility Trajectories. Sensors, 19.","DOI":"10.3390\/s19092102"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"127","DOI":"10.4017\/gt.2014.12.3.001.00","article-title":"Automated detection of wandering patterns in people with dementia","volume":"12","author":"Vuong","year":"2014","journal-title":"Gerontechnology"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3520\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:03:42Z","timestamp":1760162622000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,19]]},"references-count":33,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21103520"],"URL":"https:\/\/doi.org\/10.3390\/s21103520","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,5,19]]}}}