Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach

Society is rapidly accepting the use of video cameras in many new and varied locations, but effective methods to utilize and manage the massive resulting amounts of visual data are only slowly developing. This paper presents a framework for live video analysis in which the behaviors of surveillance...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 33; H. 11; S. 2287 - 2301
Hauptverfasser:	Morris, B. T., Trivedi, M. M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.11.2011 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	abnormality detection activity prediction Applied sciences Artificial intelligence Computer science; control theory; systems Construction Exact sciences and technology Hidden Markov models Intelligence Learning Mathematical models Pattern analysis Pattern recognition. Digital image processing. Computational geometry Probabilistic logic real-time activity analysis Regression Retraining Sparse matrices Studies Surveillance Training Trajectories Trajectory Trajectory clustering trajectory learning Recurrence Vocabulary Image processing activity prediction Modeling Adaptive method trajectory learning Incomplete information Localization Time analysis Monitoring Mixed distribution Cluster Video cameras Real time Long term Missing data real-time activity analysis Image analysis Gaussian process Surveillance Trajectory clustering abnormality detection Scene analysis Hidden Markov model Maximum likelihood
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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