{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:00:32Z","timestamp":1772726432866,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,6,30]],"date-time":"2017-06-30T00:00:00Z","timestamp":1498780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61179067"],"award-info":[{"award-number":["61179067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61179067"],"award-info":[{"award-number":["61179067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1333110"],"award-info":[{"award-number":["U1333110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Spanish ministry MINECO","award":["TIN2016-75866-C3-3-R"],"award-info":[{"award-number":["TIN2016-75866-C3-3-R"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this paper, we propose to improve trajectory shape analysis by explicitly considering the speed attribute of trajectory data, and to successfully achieve anomaly detection. The shape of object motion trajectory is modeled using Kernel Density Estimation (KDE), making use of both the angle attribute of the trajectory and the speed of the moving object. An unsupervised clustering algorithm, based on the Information Bottleneck (IB) method, is employed for trajectory learning to obtain an adaptive number of trajectory clusters through maximizing the Mutual Information (MI) between the clustering result and a feature set of the trajectory data. Furthermore, we propose to effectively enhance the performance of IB by taking into account the clustering quality in each iteration of the clustering procedure. The trajectories are determined as either abnormal (infrequently observed) or normal by a measure based on Shannon entropy. Extensive tests on real-world and synthetic data show that the proposed technique behaves very well and outperforms the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/e19070323","type":"journal-article","created":{"date-parts":[[2017,7,3]],"date-time":"2017-07-03T10:27:31Z","timestamp":1499077651000},"page":"323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5535-2420","authenticated-orcid":false,"given":"Yuejun","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University, Yaguan Road #135, Tianjin 300350, China"},{"name":"Graphics and Imaging Lab, Universitat de Girona, Campus Montilivi, 17071 Girona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8243-6670","authenticated-orcid":false,"given":"Qing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University, Yaguan Road #135, Tianjin 300350, China"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University, Yaguan Road #135, Tianjin 300350, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2164-6858","authenticated-orcid":false,"given":"Mateu","family":"Sbert","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University, Yaguan Road #135, Tianjin 300350, China"},{"name":"Graphics and Imaging Lab, Universitat de Girona, Campus Montilivi, 17071 Girona, Spain"}]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University, Yaguan Road #135, Tianjin 300350, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s00138-007-0077-z","article-title":"How close are we to solving the problem of automated visual surveillance?","volume":"19","author":"Dee","year":"2008","journal-title":"Mach. 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