Pedestrians counting and event detection in crowded environment

Crowd density estimation and pedestrian counting are becoming an area of interest such as assessing the social effect and impact between small groups of people within a crowd. Still, existing experimental crowd analyses performed by operators are time consuming. Generally, human controllers are engaged to achieve this task, however, more and more, visual surveillance are becoming an essential need, it is a hard task to watch and study all recorded video due to the huge number of cameras being installed. Currently, imageprocessing field has attracted all academic and research to develop automatic counting and monitoring algorithms. In this thesis, some novel contributions in different fields are presented: pedestrian counting, event detection, and queue monitoring. Firstly, this thesis presents an original contribution in the pedestrian counting domain. In recent years, many of proposed counting techniques have used global features to estimate crowd density. In this thesis, a new approach has been introduced to replace global image features by the low levelfeatures, which are specific to individuals and clusters within the crowd. Thus, the total number of pedestrians is the summation of all clusters, which construct the crowd. Experimental results through different datasets showed that low-level features have performed better than global features. In addition to the pedestrian counting, this thesis presents another contribution in the area of pedestrian flow monitoring through the developing of a virtual door algorithm, in which pedestrians are counted while they are passing through a proposed virtual count line. Important features have been extracted from the region of interest. Discriminant features are detected, and optical flow of these points are assembled .The proposed system assembles optical flow in the trajectory direction in a discrete group of extracted feature points. Finally, this thesis presents a novel technique for estimating queue parameters, such as number of entrance, leaving and the frequency, in order to obtain a clear picture about the queue traffic and flow. Therefore, in order to obtain these parameters, the proposed pedestrian counting and virtual door approach have been integrated together. Experimental results conducted demonstrate that the proposed system is strong to real-life environments