Abstract
In this paper, we propose an algorithm for abnormal crowd behavior detection and simulation for real time surveillance applications. Our method is a low computational cost approach based on moved pixel density modelling. Using statistical methods, we obtain the model of pixel densities in normal behaviors based on datasets available in the literature. During abnormal anomalous event detection we run a simulation of people motion and save the data for future analysis. We test the execution time of our algorithm for motion detection to validate its usage in fast applications. Finally we validate our method comparing it with other approaches in the literature in two datasets.
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Acknowledgement
This work is part of the projects VisualNavDrone 2016-PIC-024 and MultiNavCar 2016-PIC-025, from the Universidad de las Fuerzas Armadas ESPE, directed by Dr. Wilbert G. Aguilar.
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Aguilar, W.G. et al. (2017). Real-Time Detection and Simulation of Abnormal Crowd Behavior. In: De Paolis, L., Bourdot, P., Mongelli, A. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2017. Lecture Notes in Computer Science(), vol 10325. Springer, Cham. https://doi.org/10.1007/978-3-319-60928-7_36
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DOI: https://doi.org/10.1007/978-3-319-60928-7_36
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