Detection of Abnormal Movements of a Crowd in a Video Scene
Garrab Mariem, E. Ridha, Z. Mourad · International Journal of Computer Theory and Engineering · 2015
There are many applications for the detection of anomalies; in this paper we propose a new method for the detection of abnormalities in crowded scenes.In our method, we present a hand technique for temporal tracking of different people during movement in a video sequence, using the technique of Gaussian mixture model (GMM).This method is based on the blob detector analyzing foreground functioning of cells based on which is created a statistical modelling of the individual item.The Gaussian mixture model (GMM) is used as a position vector to extract different motion characteristics.In addition to detecting the abnormal behavior of the crowd we propose a new simple method.We use the differential method of Lucas and Kanade to estimate abnormal events observed in a surveillance video.It presents an algorithm to accelerate the process of abnormal motion detection based on a local adjustment of the velocity field by calculating the light intensity between two images to detect the abnormal movement.