Int. J. Applied Pattern Recognition, Vol. 5, No. 2, 2018 101 Copyright © 2018 Inderscience Enterprises Ltd. Crowd events recognition in a video without threshold value setting Hocine Chebi* Faculty of Hydrocarbons and Chemistry, M’Hamed BOUGARA University of Boumerdés, Laboratory of Applied Automation, Avenue de l’Indépendance, 35000, Boumerdès, Algeria Email: chebi.hocine@yahoo.fr Email: chebi.hocine@univ-boumerdes.dz *Corresponding author Dalila Acheli Faculty of Engineering, M’Hamed BOUGARA University of Boumerdés, Laboratory of Applied Automation, Avenue de l’Indépendance, 35000, Boumerdès, Algeria Email: dacheli2000@yahoo.fr Email: d.acheli@univ-boumerdes.dz Mohamed Kesraoui Faculty of Hydrocarbons and Chemistry, M’Hamed BOUGARA University of Boumerdés, Laboratory of Applied Automation, Avenue de l’Indépendance, 35000, Boumerdès, Algeria Email: mkesra@univ-boumerdes.dz Abstract: Behavioural recognition and prediction of people’s activities since video present major concerns in the field of computer vision. The main objective of the proposed work is the introduction of a new algorithm which allows analysing objects in motion from the video to extract human behaviours in a complex environment. This analysis is carried out for the indoor or the outdoor environments filmed by simple means of detection (surveillance camera). The analysed scene presents in a group of people, one distinguishes the crowd scenes for an important number of people. In this type of scene, we are interested in the problems of crowd event detection by an automatic technique without setting the threshold value by neural networks to detect several anomalies in a crowd scene. To achieve these objectives, we propose a calculation of covariance and automatic artificial neural networks-based approach in order to detect several anomalies. Experiment validation has been done based on known data, where in a satisfactory results has been obtained comparing to some previous works mentioned in the state-of-the-art. Keywords: visual analysis; crowd behaviour; intelligent video surveillance; anomaly; artificial neurons networks; ANN; automatic recognition.