a discrete choice modeling framework for pedestrian walking behavior with application to human tracking in video sequences

Intelligent Transportation Systems (ITS) have triggered important research activities in the context of behavioral dynamics. Several new models and simulators for driving and travel behaviors, along with new integrated systems to manage various elements of ITS, have been proposed in the past decades. In this context, less attention has been given to pedestrian modeling and simulation. In 2001, the first international conference on Pedestrian and Evacuation Dynamics took place in Duisburg, Germany, showing the recent, growing interest in pedestrian simulation and modeling in the scientific community. The ability of predicting the movements of pedestrians is valuable indeed in many contexts. Architects are interested in understanding how individuals move into buildings to find out optimality criteria for space design. Transport engineers face the problem of integration of transportation facilities, with particular emphasis on safety issues for pedestrians. Recent tragic events have increased the interest for automatic video surveillance systems, able to monitoring pedestrian flows in public spaces, throwing alarms when abnormal behaviors occur. In this spirit, it is important to define mathematical models based on specific (and context-dependent) behavioral assumptions, tested by means of proper statistical methods. Data collection for pedestrian dynamics is particularly difficult and few models presented in literature have been calibrated and validated on real datasets. Pedestrian behavior can be modelled at various scales. This work addresses the problem of pedestrian walking behavior modeling, interpreting the walking process as a sequence of choices over time. People are assumed to be rational decision makers. They are involved in the process of choosing their next position in the surrounding space, as a function of their kinematic characteristics and reacting to the presence of other individuals. We choose a mathematical framework based on discrete choice analysis, which provides a set of well founded econometric tools to model disaggregate phenomena. The pedestrian model is applied in a computer vision application, namely detection and tracking of pedestrians in video sequences. A methodology to integrate behavioral and image-based information is proposed. The result of this approach is a dynamic detection of the individuals in the video sequence. We do not make a clear cut between detection and tracking, which are rather thought as inter-operating procedures, in order to generate a set of hypothetical pedestrian trajectories, evaluated with the proposed model, exploiting both dynamic and behavioral information. The main advantage applying such methodology is given by the fact that the standard target detection/recognition step is bypassed, reducing the complexity of the system, with a consistent gain in computational time. On the other hand, the price to pay as a consequence for the simple initialization procedure is the overestimation of the number of targets. In order to reduce the bias in the targets’ number estimation, a comparative study between different approaches, based on clustering techniques, is proposed.