Abnormal behavior detection based on dense trajectories
Shatalin R.A., Fidelman V.R., Ovchinnikov P.E.


Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia


In this paper, we propose abnormal behavior detection algorithms based on dense trajectories and principal components for video surveillance applications. The result shows that the proposed algorithms are faster than an algorithm based on lengths of displacement vectors but the accuracy is only retained if the bag-of-features model is trained on a balanced sample of behavior features.

video surveillance, abnormal behaviour detection, principal component analysis, dense trajectories.

Shatalin RA, Fidelman VR, Ovchinnikov PE. Abnormal behavior detection based on dense trajectories. Computer Optics 2018; 42(3): 476-482. DOI: 10.18287/2412-6179-2018-42-3-476-482.


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