Many of the state-of-the-art approaches for automatic abnormal behavior detection in crowded scenes are based on complex models which require high processing time and several parameters to be adjusted. This paper presents a simple new approach that uses background subtraction algorithm and optical flow to encode the normal behavior pattern through a Gaussian Mixture Model (GMM). Abnormal behavior is detected comparing new samples against the mixture model. Experimental results on standards anomaly detection and localization benchmarks are presented and compared to other algorithms considering detection rate and processing time.
CITATION STYLE
Rojas, O. E., & Tozzi, C. L. (2016). Abnormal crowd behavior detection based on gaussian mixture model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9914 LNCS, pp. 668–675). Springer Verlag. https://doi.org/10.1007/978-3-319-48881-3_47
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