Abstract
In this paper we present a number of methods (manual, semi-automatic and automatic) for tracking individual targets in high density crowd scenes where thousands 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 spatiotemporal 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. Tracking in such places where pedestrian density reaches 7 to 8 persons/m2 is extremely challenging due to the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. The tracking method which is outlined in this paper overcomes these challenges by using a virtual camera which is matched in position, rotation and focal length to the original camera in such a way that the features of the 3D-model match the feature position of the filmed mosque. In this model an individual feature has to be identified by eye, where contrast is a criterion. We do know that the pilgrims walk on a plane, and after matching the camera we also have the height of the plane in 3D-space from our 3D-model. A point object is placed at the position of a selected pedestrian. During the animation we set multiple animation-keys (approximately every 25 to 50 frames which equals 1 to 2 seconds) for the position, such that the position of the point and the pedestrian overlay nearly at every time. By combining all these variables with the available appearance information, we are able to track individual targets in high density crowds.
Highlights
Crowd simulation has found its way into computer science, computer visualizations and the computer simulation of oriented building construction and crowd management (Predtetschenski & Milinski, Russ: 1969, Germ: 1971)
For the evolution and development of pedestrian microscopic simulation models, a lot of data was collected with the help of video recording and tracking of moving entities in the pedestrian flow using the coordinates of the head path that was established through image processing
One of the significant challenges in the planning, design and management of public facilities subject to high density crowd dynamics and pedestrian traffic are the shortcomings in the empirical data
Summary
The electronic and digital revolution in video techniques during recent years has made it possible to gather detailed data concerning pedestrian behaviour, both in experiments and in real life situations (Hoogendoorn & Daamen, 2005, Helbing et al, 2008; Boltes et al, 2010). The big challenge is to develop a new efficient method of defining and measuring basic quantities like density, flow and speed. Basic quantities of pedestrian dynamics are the density ρ [1/m2] in an area A and the velocity v [m/s] of persons or a group of persons, and the flow through a door or across a specific line Q = v (r,t ) ρ (r,t ) [1/s]. The task is to improve the given methods such that they allow going fairly close to the real data of the crowd quantities. Since pedestrians move slower in a region of high density, the simulated particles should update their speed with the surrounding circumstances to maximize their rate of progress towards their goals
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