{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:19:06Z","timestamp":1761895146401,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2010,8,11]],"date-time":"2010-08-11T00:00:00Z","timestamp":1281484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings. In the second stage, using sequences of the recorded states, a stochastic graph model based on a Markovian approach is built. A behavior is labeled abnormal when current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. The approach is tested with several sequences of images acquired from a vehicular intersection where the traffic flow and duration used in connection with the traffic lights are continuously changed throughout the day. Finally, the low complexity and the flexibility of the approach make it reliable for use in real time systems.<\/jats:p>","DOI":"10.3390\/s100807576","type":"journal-article","created":{"date-parts":[[2010,8,12]],"date-time":"2010-08-12T02:58:15Z","timestamp":1281581895000},"page":"7576-7601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Detecting Abnormal Vehicular Dynamics at Intersections Based on an Unsupervised Learning Approach and a Stochastic Model"],"prefix":"10.3390","volume":"10","author":[{"given":"Hugo","family":"Jim\u00e9nez-Hern\u00e1ndez","sequence":"first","affiliation":[{"name":"Centro de Ingenier\u00eda y Desarrollo Industrial, Av. Pie de la Cuesta No. 702, Desarrollo San Pablo, Quer\u00e9taro, Mexico"}]},{"given":"Jose-Joel","family":"Gonz\u00e1lez-Barbosa","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Ciencia Aplicada y Tecnolog\u00eda Avanzada. 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Advances in Cooperative Multi-Sensor Video Surveillance, Carnegie Mellon University and Stanford Corporation, IEEE Computer Society. Technical report."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/34.868684","article-title":"A bayesian computer vision system for modeling human interactions","volume":"22","author":"Oliver","year":"2000","journal-title":"IEEE Trans. Patt. Anal. Mach. Int"},{"key":"ref_5","unstructured":"Rao, C, Shah, M, and Syeda-Mahmood, T (2003, January 2\u20138). Action Recognition based on View Invariant Spatio-Temporal Analysis. Berkeley, CA, USA."},{"key":"ref_6","first-page":"30777","article-title":"Semantic Interpretation of Object Activities in a Surveillance System","volume":"3","author":"Lou","year":"2002","journal-title":"IEEE Int. Conf. Patt. Recog"},{"key":"ref_7","first-page":"663","article-title":"Principal axis-based correspondence between multiple cameras for people tracking","volume":"26","author":"Hu","year":"2006","journal-title":"IEEE Trans. Patt. Anal. Mach. Int"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"The mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst.Tech. J"},{"key":"ref_9","first-page":"19","article-title":"Generalized kolmogorov complexity and duality in theory of computations","volume":"25","author":"Burgin","year":"1982","journal-title":"Notic. Russ. Acad. Sci"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1147\/rd.214.0350","article-title":"Algorithmic information theory","volume":"21","author":"Chaitin","year":"1977","journal-title":"IBM J. Res. Dev"},{"key":"ref_11","unstructured":"Mackay, D (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/34.868685","article-title":"Discovery and segmentation of activities in video","volume":"22","author":"Brand","year":"2000","journal-title":"IEEE Trans. Patt. Anal. Mach. Int"},{"key":"ref_13","unstructured":"Tomasi, C, and Shi, J (July, January 27). Good features to track. Seattle, WA, USA."},{"key":"ref_14","unstructured":"Lucas, B, and Kanade, T (, January April). An iterative image registration technique with an application to stereo vision. Washintong, DC, USA."},{"key":"ref_15","unstructured":"Lopes, R, Reid, I, and Hobson, P (2007, January 23\u201327). The two-dimensional Kolmogorov-Smirnov test. Amsterdam, the Netherlands."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. Royal Stat. Soci"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Park, JM, and Lu, Y (2008). Edge detection in grayscale, color, and range images. Wiley Encyclopedia Comput Sci Engin.","DOI":"10.1002\/9780470050118.ecse603"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Soille, P (1999). Morphological Image Analysis: Principles and Applications, Springer-Verlag.","DOI":"10.1007\/978-3-662-03939-7"},{"key":"ref_19","unstructured":"Kanerva, P (1998). Sparse Distributed Memory, MIT Press."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/34.790428","article-title":"Similarity measures","volume":"21","author":"Santini","year":"1999","journal-title":"IEEE Trans. Patt. Anal. Mach. Int"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1024476403183","article-title":"A theoretical tour of connectivity in image processing and analysis","volume":"19","author":"Goutsias","year":"2003","journal-title":"J. Math. Imaging Vision"},{"key":"ref_22","unstructured":"Barron, J, Fleet, D, Beauchemin, S, and Burkitt, T (1992, January 15\u201318). Performance of optical flow techniques. Champaign, IL, USA."},{"key":"ref_23","unstructured":"Tsutui, H, Miura, J, and Shirai, Y (2001, January 20\u201322). Optical flow-based person tracking by multiple camera. Kauai, HI, USA."},{"key":"ref_24","unstructured":"Fleet, D, and Weiss, Y (2006). Handbook of Mathematical Models in Computer Vision, Springer."},{"key":"ref_25","unstructured":"Serra, J (2004). Viscous Lattices, rue Saint-Honor. Technical report; Centre de Morphologie Math\u00e9matique Ecole National Superiure des Mines Paris, 35."},{"key":"ref_26","unstructured":"Wolpert, DH (2001, January 10\u201324). The supervised learning no-free-lunch theorems. On the World Wide Web."},{"key":"ref_27","unstructured":"Porikli, F (2004, January 11\u201314). Trajectory pattern detection by HMM parameter space features and eigenvector Clustering. Prague, Czech Republic."},{"key":"ref_28","first-page":"955","article-title":"Learning and detecting activities from movement trajectories using the hierarchical hidden markov models","volume":"2","author":"Nguyen","year":"2005","journal-title":"IEEE Conf. Comput. Vision Patt. Recog"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1214\/aoms\/1177697196","article-title":"A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains","volume":"41","author":"Baum","year":"1970","journal-title":"Ann. Math. Statist"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113","DOI":"10.2307\/2017458","article-title":"Notes on Existence and Necessity","volume":"40","author":"Quine","year":"1943","journal-title":"J. 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