Petri Net Models for Event Recognition in Surveillance Videos

Video surveillance is the process of monitoring the behavior of people and objects within public places, e.g. airports and traffic intersections, by means of visual aids (cameras) usually for safety and security purposes. As the amount of video data gathered daily by surveillance cameras increases, the need for automatic systems to detect and recognize suspicious activities performed by people and objects is also increasing. The first part of the thesis describes a framework for modeling and recognition of events from surveillance video. Our framework is based on deterministic inference using Petri nets. Events can be composed by combining primitive events and previously defined events by spatial, temporal and logical relations. We provide a graphical user interface (GUI) to formulate such event models. Our approach automatically maps each of these models into a set of Petri net filters that represent the components of the event. Lower-level video processing modules, e.g. background subtraction, tracking and classification, are used to detect the occurrence of primitive events. These primitive events are then filtered by Petri nets filters to recognize composite events of interest. Our framework is general enough and we have applied it to many surveillance domains. In the second part of the thesis, we address the problem of detecting carried objects. Detecting carried objects is the main step to solve the problem of left object detection. We present two approaches to the left object detection problem. Both approaches poses the problem as a classification problem. For both approaches, we trained SVM classifiers on a laboratory database that contains examples of people seen with and without two common objects, namely backpacks and suitcases. We used a boosting technique, AdaBoost, to select the most discriminative features used by the SVMs and to enhance the performance of the classifiers. We give recognition results for each approach and then compare both approaches and describe the advantages of each one. We also compare the performance of both approaches on real world videos captured at the Munich airport

Human and Group Activity Recognition from Video Sequences

A good solution to human activity recognition enables the creation of a wide variety of useful applications such as applications in visual surveillance, vision-based Human-Computer-Interaction (HCI) and gesture recognition. In this thesis, a graph based approach to human activity recognition is proposed which models spatio-temporal features as contextual space-time graphs. In this method, spatio-temporal gradient cuboids were extracted at significant regions of activity, and feature graphs (gradient, space-time, local neighbours, immediate neighbours) are constructed using the similarity matrix. The Laplacian representation of the graph is utilised to reduce the computational complexity and to allow the use of traditional statistical classifiers. A second methodology is proposed to detect and localise abnormal activities in crowded scenes. This approach has two stages: training and identification. During the training stage, specific human activities are identified and characterised by employing modelling of medium-term movement flow through streaklines. Each streakline is formed by multiple optical flow vectors that represent and track locally the movement in the scene. A dictionary of activities is recorded for a given scene during the training stage. During the testing stage, the consistency of each observed activity with those from the dictionary is verified using the Kullback-Leibler (KL) divergence. The anomaly detection of the proposed methodology is compared to state of the art, producing state of the art results for localising anomalous activities. Finally, we propose an automatic group activity recognition approach by modelling the interdependencies of group activity features over time. We propose to model the group interdependences in both motion and location spaces. These spaces are extended to time-space and time-movement spaces and modelled using Kernel Density Estimation (KDE). The recognition performance of the proposed methodology shows an improvement in recognition performance over state of the art results on group activity datasets

Contributions for the automatic description of multimodal scenes

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Reconnaissance comportementale et suivi multi-cible dans des environnements partiellement observés

In this thesis, we are interested in the problem of pedestrian behavioral tracking within a critical environment partially under sensory coverage. While most of the works found in the literature usually focus only on either the location of a pedestrian or the activity a pedestrian is undertaking, we stands in a general view and consider estimating both data simultaneously. The contributions presented in this document are organized in two parts. The first part focuses on the representation and the exploitation of the environmental context for serving the purpose of behavioral estimation. The state of the art shows few studies addressing this issue where graphical models with limited expressiveness capacity such as dynamic Bayesian networks are used for modeling prior environmental knowledge. We propose, instead, to rely on richer contextual models issued from autonomous agent-based behavioral simulators and we demonstrate the effectiveness of our approach through extensive experimental evaluations. The second part of the thesis addresses the general problem of pedestrians’ mutual influences, commonly known as targets’ interactions, on their respective behaviors during the tracking process. Under the assumption of the availability of a generic simulator (or a function) modeling the tracked targets' behaviors, we develop a yet scalable approach in which interactions are considered at low computational cost. The originality of the proposed approach resides on the introduction of density-based aggregated information, called ‘’representatives’’, computed in such a way to guarantee the behavioral diversity for each target, and on which the filtering system relies for computing, in a finer way, behavioral estimations even in case of occlusions. We present the modeling choices, the resulting algorithms as well as a set of challenging scenarios on which the proposed approach is evaluated.Dans cette thèse, nous nous intéressons au problème du suivi comportemental des piétons au sein d'un environnement critique partiellement observé. Tandis que plusieurs travaux de la littérature s'intéressent uniquement soit à la position d'un piéton dans l'environnement, soit à l'activité à laquelle il s'adonne, nous optons pour une vue générale et nous estimons simultanément à ces deux données. Les contributions présentées dans ce document sont organisées en deux parties. La première partie traite principalement du problème de la représentation et de l'exploitation du contexte environnemental dans le but d'améliorer les estimations résultant du processus de suivi. L'état de l'art fait mention de quelques études adressant cette problématique. Dans ces études, des modèles graphiques aux capacités d'expressivité limitées, tels que des réseaux Bayésiens dynamiques, sont utilisés pour modéliser des connaissances contextuelles a priori. Dans cette thèse, nous proposons d'utiliser des modèles contextuelles plus riches issus des simulateurs de comportements d'agents autonomes et démontrons l’efficacité de notre approche au travers d'un ensemble d'évaluations expérimentales. La deuxième partie de la thèse adresse le problème général d'influences mutuelles - communément appelées interactions - entre piétons et l'impact de ces interactions sur les comportements respectifs de ces derniers durant le processus de suivi. Sous l'hypothèse que nous disposons d'un simulateur (ou une fonction) modélisant ces interactions, nous développons une approche de suivi comportemental à faible coût computationnel et facilement extensible dans laquelle les interactions entre cibles sont prises en compte. L'originalité de l'approche proposée vient de l'introduction des ``représentants'', qui sont des informations agrégées issues de la distribution de chaque cible de telle sorte à maintenir une diversité comportementale, et sur lesquels le système de filtrage s'appuie pour estimer, de manière fine, les comportements des différentes cibles et ceci, même en cas d'occlusions. Nous présentons nos choix de modélisation, les algorithmes résultants, et un ensemble de scénarios difficiles sur lesquels l’approche proposée est évaluée

Cities in Asia by and for the People

This book examines the active role of urban citizens in constructing alternative urban spaces as tangible resistance towards capitalist production of urban spaces that continue to encroach various neighborhoods. The collection of narratives presented here brings together research from ten different Asian cities and re-theorises the city from the perspective of ordinary people facing moments of crisis, contestations, and cooperative quests to create alternative spaces to those being produced under prevailing urban processes. The chapters accent the exercise of human agency through daily practices in the production of urban space and the intention is not one of creating a romantic or utopian vision of what a city "by and for the people" ought to be. Rather, it is to place people in the centre as mediators of city-making with discontents about current conditions and desires for a better life

Bowdoin Orient v.132, no.1-24 (2000-2001)

https://digitalcommons.bowdoin.edu/bowdoinorient-2000s/1001/thumbnail.jp