Multi-layer hierarchical clustering of pedestrian trajectories for automatic counting of people in video sequences

In this paper we propose an approach to count the number of pedestrians, given a trajectory data set provided by a tracking system. The tracking process itself is treated as a black box providing us the input data. The idea is to apply a hierarchical clustering algorithm, using different data representations and distance measures, as a post-processing step. The final goal is to reduce the difference between the number of tracked pedestrians and the real number of individuals present in the scene

Adaptive human motion analysis and prediction

Human motion analysis and prediction is an active research area where predicting human motion is often performed for a single time step based on historical motion. In recent years, longer term human motion prediction has been attempted over a number of future time steps. Most current methods learn motion patterns (MPs) from observed trajectories and then use them for prediction. However, these learned MPs may not be indicative due to inadequate observation, which naturally affects the reliability of motion prediction. In this paper, we present an adaptive human motion analysis and prediction method. It adaptively predicts motion based on the classified MPs in terms of their credibility, which refers to how indicative the learned MPs are for the specific environment. The main contributions of the proposed method are as follows: First, it provides a comprehensive description of MPs including not only the learned MPs but also their evaluated credibility. Second, it predicts long-term future motion with reasonable accuracy. A number of experiments have been conducted in simulated scenes and real-world scenes and the prediction results have been quantitatively evaluated. The results show that the proposed method is effective and superior in its performance when compared with a recursively applied Auto-Regressive (AR) model, which is called the Recursive Short-term Predictor (RSP) for long-term prediction. The proposed method has 17.73% of improvement over the RSP in prediction accuracy in the experiment with the best performance. On average, the proposed method has 5% improvement over the RSP in prediction accuracy over 10 experiments. © 2011 Elsevier Ltd. All rights reserved.postprin

Dropped object detection in crowded scenes

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 83-85).In the last decade, the topic of automated surveillance has become very important in the computer vision community. Especially important is the protection of critical transportation places and infrastructure like airport and railway stations. As a step in that direction, we consider the problem of detecting abandoned objects in a crowded scene. Assuming that the scene is being captured through a mid-field static camera, our approach consists of segmenting the foreground from the background and then using a change analyzer to detect any objects which meet certain criteria. In this thesis, we describe a background model and a method of bootstrapping that model in the presence of foreign objects in the foreground. We then use a Markov Random Field formulation to segment the foreground in image frames sampled periodically from the video camera. We use a change analyzer to detect foreground blobs that remain static through the scene and based on certain rules decide if the blob could be a potentially abandoned object.by Deepti Bhatnagar.S.M

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

Caracterización semántica de espacios: Sistema de Videovigilancia Inteligente en Smart Cities

Esta Tesis Doctoral, realizada dentro del proyecto europeo HuSIMS - Human Situation Monitoring System, presenta una metodología inteligente para la caracterización de escenarios capaz de detectar e identificar situaciones anómalas analizando el movimiento de los objetos. El sistema está diseñado para reducir al mínimo el procesamiento y la transmisión de vídeo permitiendo el despliegue de un gran número de cámaras y sensores, y por lo tanto adecuada para Smart Cities. Se propone un enfoque en tres etapas. Primero, la detección de objetos en movimiento en las propias cámaras, utilizando algorítmica sencilla, evitando el envío de datos de vídeo. Segundo, la construcción de un modelo de las zonas de las escenas utilizando los parámetros de movimiento identificados previamente. Y tercero, la realización de razonado semántico sobre el modelo de rutas y los parámetros de los objetos de la escena actual para identificar las alarmas reconociendo la naturaleza de los eventosDepartamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic