Automatic human behaviour anomaly detection in surveillance video

This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic human behaviour anomaly detection we address the challenge of interpreting behaviour within the context of the social and physical environment. We develop and evaluate a process for measuring social connectivity between individuals in a scene using motion and visual attention features. To do this we use mutual information and Euclidean distance to build a social similarity matrix which encodes the social connection strength between any two individuals. We de- velop a second contextual basis which acts by segmenting a surveillance environment into behaviourally homogeneous subregions which represent high tra c slow regions and queuing areas. We model the heterogeneous scene in homogeneous subgroups using both contextual elements. We bring the social contextual information, the scene context, the motion, and visual attention features together to demonstrate a novel human behaviour anomaly detection process which nds outlier behaviour from a short sequence of video. The method, Nearest Neighbour Ranked Outlier Clusters (NN-RCO), is based upon modelling behaviour as a time independent se- quence of behaviour events, can be trained in advance or set upon a single sequence. We nd that in a crowded scene the application of Mutual Information-based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in all the datasets we test upon. We additionally demonstrate that our work is applicable to other data domains. We demonstrate upon the Automatic Identi cation Signal data in the maritime domain. Our work is capable of identifying abnormal shipping behaviour using joint motion dependency as analogous for social connectivity, and similarly segmenting the shipping environment into homogeneous regions

Recreation, tourism and nature in a changing world : proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 2010

Proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 201

Exploration and Mapping of Spatio-Temporal Pedestrian Flow Patterns for Mobile Robots

Socially compliant robot navigation is one of the key aspects for long-term acceptance of mobile robots in human-populated environments. One of the current barriers for this acceptance is that many navigation methods are based only on reactive behaviours, which can lead to frequent re-plannings, causing an erratic or aggressive robot behaviour. Instead, giving the ability to model and predict in advance how the people are likely to behave, from a long-term perspective, is an important enabler for safe and efficient navigation. For example, a robot may use its knowledge of the expected human motion to go with the main direction of flow to minimise the possibility of collisions or trajectory re-plannings. In order to provide robots with knowledge of the expected activity patterns of people at different places and times,the first main contribution of this thesis is the introduction of a Spatio-Temporal Flow map (STeF-map). This is a time-dependent probabilistic map able to model and predict the flow patterns of people in the environment. The proposed representation models the likelihood of motion directions on a grid-based map by a set of harmonic functions, which efficiently capture long-term variations of crowd movements over time. The experimental evaluation shows that the proposed model enables a better human motion prediction than spatial-only approaches and an increased capacity for socially compliant robot navigation. Obtaining this knowledge from a mobile robot platform is, however, not a trivial task, as usually they can only observe a fraction of the environment at a time, while the activity patterns of people may also change at different times. Therefore, the second main contribution is the investigation of a new methodology for mobile robot exploration to maximise the knowledge of human activity patterns, by deciding where and when to collect observations based on an exploration policy driven by the entropy levels in a spatio-temporal map. The evaluation is performed by simulating mobile robot exploration using real sensory data from three long-term pedestrian datasets, and the results show that for certain scenarios, the proposed exploration system can learn STeF-maps more quickly and better predict the flow patterns than uninformed strategies

The Effectiveness of the Stylometry of Function Words in Discriminating between Shakespeare and Fletcher

A number of recent successful authorship studies have relied on a statistical analysis of language features based on function words. However, stylometry has not been extensively applied to Elizabethan and Jacobean dramatic questions. To determine the effectiveness of such an approach in this field, language features are studied in twenty-four plays by Shakespeare and eight by Fletcher. The goal is to develop procedures that might be used to determine the authorship of individual scenes in The Two Noble Kinsmen and Henry VIII. Homonyms, spelling variants and contracted forms in old-spelling dramatic texts present problems for a computer analysis. A program that uses a system of pre-edit codes and replacement /expansion lists was developed to prepare versions of the texts in which all forms of common words can be recognized automatically. To evaluate some procedures for determining authorship developed by A. Q. Morton and his colleagues, occurrences of 30 common collocations and 5 proportional pairs are analyzed in the texts. Within-author variation for these features is greater than had been found in previous studies. Univariate chi-square tests are shown to be of limited usefulness because of the statistical distribution of these textual features and correlation between pairs of features. The best of the collocations do not discriminate as well as most of the individual words from which they are composed. Turning to the rate of occurrence of individual words and groups of words, distinctiveness ratios and t-tests are used to select variables that best discriminate between Shakespeare and Fletcher. Variation due to date of composition and genre within the Shakespeare texts is examined. A multivariate and distributionfree discriminant analysis procedure (using kernel estimation) is introduced. The classifiers based on the best marker words and the kernel method are not greatly affected by characterization and perform well for samples as short as 500 words. When the final procedure is used to assign the 459 scenes of known authorship (containing at least 500 words)almost 112 95% are assigned to the correct author. Only two scenes are incorrectly classified, and 4.8% of the scenes cannot be assigned to either author by the procedure. When applied to individual scenes of at least 500 words in The Two Noble Kinsmen and Henry VIII, the procedure indicates that both plays are collaborations and generally supports the usual division. However, the marker words in a number of scenes often attributed to Fletcher are very much closer to Shakespeare's pattern of use. These scenes include TNK IV.iii and H8 I.iii, IV.i-ii and V.iv

Improved robustness and efficiency for automatic visual site monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 219-228).Knowing who people are, where they are, what they are doing, and how they interact with other people and things is valuable from commercial, security, and space utilization perspectives. Video sensors backed by computer vision algorithms are a natural way to gather this data. Unfortunately, key technical issues persist in extracting features and models that are simultaneously efficient to compute and robust to issues such as adverse lighting conditions, distracting background motions, appearance changes over time, and occlusions. In this thesis, we present a set of techniques and model enhancements to better handle these problems, focusing on contributions in four areas. First, we improve background subtraction so it can better handle temporally irregular dynamic textures. This allows us to achieve a 5.5% drop in false positive rate on the Wallflower waving trees video. Secondly, we adapt the Dalal and Triggs Histogram of Oriented Gradients pedestrian detector to work on large-scale scenes with dense crowds and harsh lighting conditions: challenges which prevent us from easily using a background subtraction solution. These scenes contain hundreds of simultaneously visible people. To make using the algorithm computationally feasible, we have produced a novel implementation that runs on commodity graphics hardware and is up to 76 faster than our CPU-only implementation. We demonstrate the utility of this detector by modeling scene-level activities with a Hierarchical Dirichlet Process.(cont.) Third, we show how one can improve the quality of pedestrian silhouettes for recognizing individual people. We combine general appearance information from a large population of pedestrians with semi-periodic shape information from individual silhouette sequences. Finally, we show how one can combine a variety of detection and tracking techniques to robustly handle a variety of event detection scenarios such as theft and left-luggage detection. We present the only complete set of results on a standardized collection of very challenging videos.by Gerald Edwin Dalley.Ph.D

Place, recreation and local development

Proceedings of the 9th International Conference on Monitoring and Management of Visitors in Recreational and Protected Areas (MMV9), Bordeaux, FRA, 29-/08/2018 - 31/08/2018It is our pleasure to welcome you to the 9th international Conference on Monitoring and Management of Visitors in Recreational and Protected Areas (MMV9) with a program including keynote speeches, organized and poster sessions, a half-day field trip, social events and post conference trips. This is the first time that France has hosted an MMV Conference. Our country is ranked as the world's top tourist destination, thanks largely to its culture, art, and gastronomy, as well as popular cities such as Paris and Bordeaux. On the other hand, France's potential as a destination for outdoor recreation and nature-based tourism is not hugely publicized, despite its many unique features in this respect: varied climate and natural assets (shoreline, mountains, lakes, and forests), large expanses of countryside, and a network of protected natural areas, to name but a few. France's protected areas are often free to access for the general public. However, in contrast with other countries, nature conservation in specific areas is much less widespread. Where it does take place, it is often centered on territories that are perceived to be "attractive", and where many conflicting activities are practiced. This may be one of the reasons why contractual tools and regional park systems are quite popular in France. The MMV Conference offers an excellent opportunity to discuss the situation in France in greater depth. The theme proposed for the conference was "recreation, place and local development". This reflects our assumption that recreational areas are not just physical assets designed to receive visitors for the purpose of leisure - which in itself would already be something of great importance - but that they reflect deeper social phenomena, as demonstrated through the range of organized sessions dedicated to discussing questions such as environmental education and economic development, but also emerging themes such as social integration, community resilience, environmental justice, and health. The traditional topics covered by MMV Conference reflect an evolving society: with innovations in monitoring techniques (both on people and nature), focus on new populations (Y generation, ethnic minority) and a larger concern for individual engagement and participative management. The 9th Edition of MMV is co-hosted by Irstea and BSA. This would not have been possible without significant contributions from a large number of additional partners and sponsors as well as our national scientific and organizing committee. We would like to take this opportunity to thank everyone for their help. After two years of planning, we are proud to announce that we have more than 160 presentations from 30 countries, meaning that the conference will host over 200 participants from across the globe. We are honored that the International Steering Committee has given us the opportunity to be part of this great MMV community, which organized its first meeting in 2002. We hope you will enjoy the conference as much as we enjoyed organizing it. If you can't be with us in person, we hope that you will enjoy reading our publications

Songs of Survival: Men in 21st Century Popular Music

Songs of Survival: Men in 21st Century Popular Music gathers together 60 essays dealing with a significant selection of male artists and their songs. The aim is to consider what men are saying through their careers, self-presentation, lyrics and videos about masculinity