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Ambient Intelligence A Novel Paradigm

:	Paolo Remagnino, Gian L.uca Foresti, Tim Ellis
:	Gian Luca Foresti, Tim Ellis
:	Ambient Intelligence A Novel Paradigm
:	Springer-Verlag
:	9780387229911
:	1
:	CHF 85.40
:

:	Betriebssysteme, Benutzeroberflächen
:	English

:	240
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Ambient Intelligence (AmI) is an integrating technology for supporting a pervasive and transparent infrastructure for implementing smart environments. Such technology is used to enable environments for detecting events and behaviors of people and for responding in a contextually relevant fashion. AmI proposes a multi-disciplinary approach for enhancing human machine interaction.

Am ient Intelligence: A Novel Paradigm is a compilation of edited chapters describing current state-of-the-art and new research techniques including those related to intelligent visual monitoring, face and speech recognition, innovative education methods, as well as smart and cognitive environments.

The authors start with a description of the iDorm as an example of a smart environment conforming to the AmI paradigm, and introduces computer vision as an important component of the system. Other computer vision examples describe visual monitoring for the elderly, classic and novel surveillance techniques using clusters of cameras installed in indoor and outdoor application domains, and the monitoring of public spaces. Face and speech recognition systems are also covered as well as enhanced LEGO blocks for novel educational purposes. The book closes with a provocative chapter on how a cybernetic system can be designed as the backbone of a human machine interaction.

Chapter 6

A DISTRIBUTED MULTIPLE CAMERA SURVEILLANCE SYSTEM (p.107-108)

T.Ellis, J.Black, M.Xu and D.Makris
Digital Imaging Research Centre (DIRC), Kingston University, UK
{t.ellis,j.black,m.xu,d.makris}@kingston.ac.uk

1. Introduction

An important capability of an ambient intelligent environment is the capacity to detect, locate and identify objects of interest. In many cases interesting can move, and in order to provide meaningful interaction, capturing and tracking the motion creates a perceptively-enabled interface, capable of understanding and reacting to a wide range of actions and activities. CCTV systems fulfill an increasingly important role in the modern world, providing live video access to remote environments. Whilst the role of CCTV has been primarily focused on rather specific surveillance and monitoring tasks (i.e. security and traffic monitoring), the potential uses cover a much wider range.

The proliferation of video security surveillance systems over the past 5-10 years, in both public and commercial environments, is extensively used to remotely monitor activity in sensitive locations and publicly accessible spaces. In town and city centres, surveillance has been acknowledged to result in significant reductions in crime. However, in order to provide comprehensive and large area coverage of anything but the simplest environments, a large number of cameras must be employed.

In complex and cluttered environments with even moderate numbers of moving objects (e.g. 10-20) the problem of tracking individual objects is significantly complicated by occlusions in the scene, where an object may be partially occluded or totally disappear from camera view for both short or extended periods of time. Static occlusion results from objects moving behind (with respect to the camera) fixed elements in the scene (e.g. walls, bushes), whilst dynamic occlusion occurs as a result of moving objects in the scene occluding each other, where targets may merge or separate (e.g. a group of people walking together).

Information can be combined from multiple viewpoints to improve reliability, particularly taking advantage of the additional information where it minimises occlusion within the field-of-view (FOV). We treat the non-visible regions between camera views as simply another type of occlusion, and employ spatio-temporal reasoning to match targets moving between cameras that are spatially adjacent. The"boundaries" of the system represent locations from which previously unseen targets can enter the network.

To aid robust tracking across the camera network requires the system to maintain a record of each target entering the system and throughout its duration. When a target disappears from any camera FOV, motion prediction, colour identification, and learnt route patterns are used to re-establish tracking when the target reappears. Each target is maintained as a persistent object in the active database and spatial and temporal reasoning are used to detect these activities and ensure that entries are not retained for indefinite periods.

This chapter describes a multi-camera surveillance network that can detect and track objects (principally pedestrians and vehicles) moving through an outdoor environment. The remainder of this chapter is divided into four sections. The first describes the architecture of our multi-camera surveillance system. The second considers the image analysis methods for detecting and tracking objects within a single camera view. The next section deals with the integration of information from multiple cameras. The final section describes the structure of the database.

	Contents	7
	Preface	9
	Foreword	11
	1 AMBIENT INTELLIGENCE	14
	1. Introduction	14
	2. The Essex approach	15
	2.1 The iDorm - A Testbed for Ubiquitous Computing and Ambient Intelligence	15
	2.2 The iDorm Embedded Computational Artifacts	17
	3. Integrating Computer Vision	21
	3.1 User Detection	22
	3.2 Estimating reliability of detection	24
	3.3 Vision in the iDorm	26
	4. Conclusions	26
	References	26
	2 TOWARDS AMBIENT INTELLIGENCE FOR THE DOMESTIC CARE OF THE ELDERLY	28
	1. Introduction	28
	2. An Integrated Supervision System	29
	2.1 E-service Based Integration Schemata	32
	3. People and Robot Localization and Tracking System	34
	3.1 System architecture and implementation	36
	4. The Plan Execution Monitoring System	39
	4.1 Representing Contingencies	42
	4.2 The Execution Monitor	43
	5. Integrating Sensing and Execution Monitoring: a Running Example	46
	6. Conclusions and Future Work	49
	References	51
	3 SCALING AMBIENT INTELLIGENCE	52
	1. Ambient Intelligence: the contribution of different disciplines	52
	2. I-BLOCKS technology	55
	3. Design process	57
	4. Scaling Ambient Intelligence at level of compositional devices: predefined activities	58
	4.1 Arithmetic training	59
	4.2 Storytelling Play Scenario	60
	4.3 Linguistic scenario	63
	5. Scaling Ambient Intelligence at level of compositional devices: free activities	65
	6. Scaling Ambient Intelligence at the level of configurable environments: future scenarios	67
	6.1 The Augmented Playground	67
	6.2 Self-reconfigurable Robots	70
	7. Discussion and conclusions	71
	References	73
	4 VIDEO AND RADIO ATTRIBUTES EXTRACTION FOR HETEROGENEOUS LOCATION ESTIMATION	76
	1. Introduction	76
	2. Related work	77
	3. Main tasks of Ambient Intelligence systems	78
	4. Architecture design	79
	4.1 Inspiration	79
	4.2 Mapping the Model into an AmI Architecture	81
	4.3 Artificial Sensing	82
	4.4 Proposed structure	82
	5. Context aware systems	84
	5.1 Location feature	85
	5.2 The formalism	86
	5.3 Alignment and Extraction of Video and Radio Object Reports	88
	6. Results	93
	6.1 The environment	93
	6.2 Results for video object extraction	93
	6.3 Results for radio object extraction	93
	6.4 Alignment results	95
	7. Conclusions	95
	8. Acknowledgments	96
	References	96
	5 DISTRIBUTED ACTIVE MULTICAMERA NETWORKS	102
	1. Introduction	102
	2. Sensing modalities	102
	3. Vision for Ambient Intelligence	103
	4. Architecture	104
	5. Tracking and object detection	105
	5.1 Object detection	105
	5.2 Tracking	106
	5.3 Appearance models	107
	5.4 Track data	108
	6. Normalization	108
	7. Multi-camera coordination	110
	8. Multi-scale image acquisition	111
	8.1 Active Head Tracking and Face Cataloging	112
	8.2 Uncalibrated, multiscale data acquisition	114
	8.3 Extensions	115
	9. Indexing Surveillance Data	115
	9.1 Visualization	116
	10. Privacy	116
	11. Conclusions	117
	References	117
	6 A DISTRIBUTED MULTIPLE CAMERA SURVEILLANCE SYSTEM	120
	1. Introduction	120
	2. System architecture	121
	3. Motion detection and single-view tracking	121
	3.1 Motion Detection	122
	3.2 Scene Models	124
	3.3 Target Tracking	125
	3.4 Partial Observation	126
	3.5 Target Reasoning	129
	4. Multi view tracking	133
	4.1 Homography Estimation	133
	4.2 Least Median of Squares	134
	4.3 Feature Matching Between Overlapping Views	135
	4.4 3D Measurements	136
	4.5 Tracking in 3D	137
	4.6 Non-Overlapping Views	139
	5. System architecture	142
	5.1 Surveillance Database	143
	6. Summary	145
	7. Appendix	147
	7.1 Kalman Filter	147
	7.2 Homography Estimation	148
	7.3 3D Measurement Estimation	149
	References	150
	7 LEARNING AND INTEGRATING INFORMATION FROM MULTIPLE CAMERA VIEWS	152
	1. Introduction	152
	1.1 Semantic Scene Model	154
	2. Learning point-based regions	156
	3. Learning traj