Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences

In this paper we address the problem of detection and tracking of pedestrians in complex scenarios. The inclusion of prior knowledge is more and more crucial in scene analysis to guarantee flexibility and robustness, necessary to have reliability in complex scenes. We aim to combine image processing methods with behavioral models of pedestrian dynamics, calibrated on real data. We introduce Discrete Choice Models (DCM) for pedestrian behavior and we discuss their integration in a detection and tracking context. The obtained results show how it is possible to combine both methodologies to improve the performances of such systems in complex sequence

Adaptive-Rate Compressive Sensing Using Side Information

We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information. Our first method utilizes extra cross-validation measurements, and the second one exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients that comprise the images in the video sequence. Instead, we use the side information to predict the number of significant coefficients in the signal at the next time instant. For each image in the video sequence, our techniques specify a fixed number of spatially-multiplexed CS measurements to acquire, and adjust this quantity from image to image. Our strategies are developed in the specific context of background subtraction for surveillance video, and we experimentally validate the proposed methods on real video sequences

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

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

Survey of detection techniques, mathematical models and simulation software in pedestrian dynamics

The study of pedestrian dynamics has become in the latest years an increasing field of research. A relevant number of technicians have been looking for improving technologies able to detect walking people in various conditions. Several researchers have dedicated their works to model walking dynamics and general laws. Many studiers have developed interesting software to simulate pedestrian behavior in all sorts of situations and environments. Nevertheless, till nowadays, no research has been carried out to analyze all the three over-mentioned aspects. The remarked lack in literature of a complete research, pointing out the fundamental features of pedestrian detection techniques, pedestrian modelling and simulation and their tight relationships, motivates the draft of this paper. Aim of the paper is, first, to provide a schematic summary of each topic. Secondly, a more detailed description of the subjects is displayed, pointing out the advantages and disadvantages of each detection technology, the working logic of each model, outlining the inputs and the provided outputs, and the main features of the simulation software. Finally, the obtained results are summarized and discussed, in order to outline the correlation among the three explained themes

Bayesian Integration of a Discrete Choice Pedestrian Behavioral Model and Image Correlation Techniques for Automatic Multi Object Tracking

In this paper we deal with the multi-object tracking problem in the particular case of pedestrians, assuming the detection step already done. We use a Bayesian framework to combine the likelihood term provided by an image correlation algorithm with a prior distribution given by a discrete choice model for pedestrian behavior, calibrated on real data. We aim to show how the combination of the image information with a model of pedestrian behavior can provide appreciable results in real and complex scenarios