Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security

Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security

Hans W. Guesgen, Stephen Marsland

Copyright: © 2013 |Pages: 378

ISBN13: 9781466636828|ISBN10: 1466636823|EISBN13: 9781466636835

DOI: 10.4018/978-1-4666-3682-8

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MLA

Guesgen, Hans W., and Stephen Marsland, editors. Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security. IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3682-8

APA

Guesgen, H. W. & Marsland, S. (Eds.). (2013). Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security. IGI Global. https://doi.org/10.4018/978-1-4666-3682-8

Chicago

Guesgen, Hans W., and Stephen Marsland, eds. Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3682-8

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Recently, the ICT field has seen a shift from machine-centered focuses to human and user knowledge-based approaches. However, as priorities shift, questions arise on how to detect and monitor users’ behavior.

Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security takes an insightful look into the applications and dependability of behavior detection. In addition, this comprehensive publication looks into the social, ethical, and legal implications of these areas. Researchers and practitioners interested in the computational aspects of behavior monitoring as well as the ethical and legal implications will find this reference source beneficial.

Table of Contents

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Front Materials

Title Page

Copyright Page

List of Reviewers

Preface

Chapters

Chapter 1

A Logic-Based Approach to Activity Recognition (pages 1-13)

Alexander Artikis, Marek Sergot, Georgios Paliouras

The authors have been developing a system for recognising human activities given a symbolic representation of video content. The input of the system is a stream of time-stamped short-term activities detected on video frames. The...

Chapter 2

Context-Aware Discovery of Human Frequent Behaviours through Sensor Information Interpretation (pages 14-32)

Asier Aztiria, Juan Carlos Augusto

The ability of discovering frequent behaviours of the users allows an environment to act intelligently, for example automating some devices’ activation. Moreover, such frequent behaviours could be used to understand and detect bad or...

Chapter 3

Towards Ontology-Based Realtime Behaviour Interpretation (pages 33-64)

Wilfried Bohlken, Patrick Koopmann, Lothar Hotz, Bernd Neumann

The authors describe a generic framework for model-based behaviour interpretation and its application to monitoring aircraft service activities. Behaviour models are represented in a standardised conceptual knowledge base using...

Chapter 4

Novelty Detection in Human Behavior through Analysis of Energy Utilization (pages 65-85)

Chao Chen, Diane J. Cook

The value of smart environments in understanding and monitoring human behavior has become increasingly obvious in the past few years. Using data collected from sensors in these environments, scientists have been able to recognize...

Chapter 5

Towards Behaviour Recognition with Unlabelled Sensor Data: As Much as Necessary, as Little as Possible (pages 86-110)

Sook-Ling Chua, Stephen Marsland, Hans W. Guesgen

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are...

Chapter 6

Tracking Systems for Multiple Smart Home Residents (pages 111-129)

Aaron S. Crandall, Diane J. Cook

Once a smart home system moves to a multi-resident situation, it becomes significantly more important that individuals are tracked in some manner. By tracking individuals, the events received from the sensor platform can then be...

Chapter 7

Sensors for Smart Homes (pages 130-156)

Anuroop Gaddam, G. Sen Gupta, S. C. Mukhopadhyay

Sensors are increasingly being employed to determine different activities of a person living at home. Numerous sensors can be used to obtain a variety of information. While many sensors may be used to make a system, it is important...

Chapter 8

Behaviour Monitoring and Interpretation: The Example of a Pedestrian Navigation System (pages 157-173)

Björn Gottfried

This chapter describes the field of Behaviour Monitoring and Interpretation, BMI for short, which defines a framework for the analysis and design of systems for the monitoring and interpretation of human behaviour. As an example...

Chapter 9

Intention-Based Decision Making via Intention Recognition and its Applications (pages 174-211)

The Anh Han, Luis Moniz Pereira

In this chapter, the authors present an intention-based decision-making system. They exhibit a coherent combination of two Logic Programming-based implemented systems, Evolution Prospection and Intention Recognition. The Evolution...

Chapter 10

Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis (pages 212-224)

Neuza Nunes, Diliana Rebelo, Rodolfo Abreu, Hugo Gamboa, Ana Fred

Time series unsupervised clustering is accurate in various domains, and there is an increased interest in time series clustering algorithms for human behavior recognition. The authors have developed an algorithm for biosignals...

Chapter 11

Monitoring Social Life and Interactions: A Sociological Perspective of Technologies (pages 225-248)

Francesca Odella

The chapter describes the sociological perspective of monitoring technologies and debates its method for analysing social implications of scientific and technical developments. It is articulated in five sections dedicated to social...

Chapter 12

Action Detection by Fusing Hierarchically Filtered Motion with Spatiotemporal Interest Point Features (pages 249-267)

YingLi Tian, Liangliang Cao, Zicheng Liu, Zhengyou Zhang

This chapter addresses the problem of action detection from cluttered videos. In recent years, many feature extraction schemes have been designed to describe various aspects of actions. However, due to the difficulty of action...

Chapter 13

From Streams of Observations to Knowledge-Level Productive Predictions (pages 268-281)

Mark Wernsdorfer, Ute Schmid

The benefit to be gained by Ambient Assisted Living (AAL) systems depends heavily on the successful recognition of human intentions. Important indicators for specific intentions are behavior and situational context. Once a sequence...

Chapter 14

Improving the Supervised Learning of Activity Classifiers for Human Motion Data (pages 282-303)

Liyue Zhao, Xi Wang, Gita Sukthankar

The ability to accurately recognize human activities from motion data is an important stepping-stone toward creating many types of intelligent user interfaces. Many supervised learning methods have been demonstrated for learning...

Chapter 15

Motion and Location-Based Online Human Daily Activity Recognition (pages 304-321)

Chun Zhu, Weihua Sheng

In this chapter, the authors propose an approach to indoor human daily activity recognition that combines motion data and location information. One inertial sensor is worn on the thigh of a human subject to provide motion data while...

Back Materials

Compilation of References

About the Contributors

Index

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