In this paper we present a number of methods (manual, semi-automatic and automatic) for tracking individual targets in high density crowd scenes where thousand of people are gathered. The necessary data about the motion of individuals and a lot of other physical information can be extracted from consecutive image sequences in different ways, including optical flow and block motion estimation. One of the famous methods for tracking moving objects is the block matching method. This way to estimate subject motion requires the specification of a comparison window which determines the scale of the estimate. In this work we present a real-time method for pedestrian recognition and tracking in sequences of high resolution images obtained by a stationary (high definition) camera located in different places on the Haram mosque in Mecca. The objective is to estimate pedestrian velocities as a function of the local density.The resulting data of tracking moving pedestrians based on video sequences are presented in the following section. Through the evaluated system the spatio-temporal coordinates of each pedestrian during the Tawaf ritual are established. The pilgrim velocities as function of the local densities in the Mataf area (Haram Mosque Mecca) are illustrated and very precisely documented.
The paper presents a moving object extraction method robust against illumination level changes for a real-time pedestrian counting system based on an x-t space-time image. This system, called the Ped-Counter, counts the number of pedestrians using TV camera images and image processing. The TV camera takes images of a path or the entrance of a store or an exhibition hall. A practical location for the counting system is under constant illumination conditions, such as under an arcade or in a room. If the system is used in other locations and under different conditions, for example, an outdoor path or an entrance where there is direct sunlight, then it must be robust against illumination level changes. The paper proposes two new processes for extracting moving objects from space-time images using background subtraction. The first is a background image reconstruction process using statistical characteristic analysis of temporal changes of a target pixel. The other is a moving object extraction process using delayed subtraction. These processes make the system robust against illumination level changes. This method is evaluated over a half-year period at an entrance experiencing drastic illumination level changes. We also confirm that this method can extract moving objects, that it is robust against season, weather, and time, and that the Ped-Counter is accurate.