12 research outputs found
Adaptive detection and tracking using multimodal information
This thesis describes work on fusing data from multiple sources of information, and focuses on two main areas: adaptive detection and adaptive object tracking in automated vision scenarios. The work on adaptive object detection explores a new paradigm in dynamic parameter selection, by selecting thresholds for object detection to maximise agreement between pairs of sources. Object tracking, a complementary technique to object detection, is also explored in a multi-source context and an efficient framework for robust tracking, termed the Spatiogram Bank tracker, is proposed as a means to overcome the difficulties of traditional histogram tracking. As well as performing theoretical analysis of the proposed methods, specific example applications are given for both the detection and the tracking aspects, using thermal infrared and visible spectrum video data, as well as other multi-modal information sources
Design of a Multi-biometric Platform, based on physical traits and physiological measures: Face, Iris, Ear, ECG and EEG
Security and safety is one the main concerns both for governments and for private
companies in the last years so raising growing interests and investments in
the area of biometric recognition and video surveillance, especially after the sad
happenings of September 2001. Outlays assessments of the U.S. government for
the years 2001-2005 estimate that the homeland security spending climbed from
100 billion of 2005. In this lapse of
time, new pattern recognition techniques have been developed and, even more
important, new biometric traits have been investigated and refined; besides
the well-known physical and behavioral characteristics, also physiological measures
have been studied, so providing more features to enhance discrimination
capabilities of individuals. This dissertation proposes the design of a multimodal
biometric platform, FAIRY, based on the following biometric traits: ear,
face, iris EEG and ECG signals. In the thesis the modular architecture of the
platform has been presented, together with the results obtained for the solution
to the recognition problems related to the different biometrics and their possible
fusion. Finally, an analysis of the pattern recognition issues concerning the
area of videosurveillance has been discussed
Design of a Multi-biometric Platform, based on physical traits and physiological measures: Face, Iris, Ear, ECG and EEG
Security and safety is one the main concerns both for governments and for private
companies in the last years so raising growing interests and investments in
the area of biometric recognition and video surveillance, especially after the sad
happenings of September 2001. Outlays assessments of the U.S. government for
the years 2001-2005 estimate that the homeland security spending climbed from
100 billion of 2005. In this lapse of
time, new pattern recognition techniques have been developed and, even more
important, new biometric traits have been investigated and refined; besides
the well-known physical and behavioral characteristics, also physiological measures
have been studied, so providing more features to enhance discrimination
capabilities of individuals. This dissertation proposes the design of a multimodal
biometric platform, FAIRY, based on the following biometric traits: ear,
face, iris EEG and ECG signals. In the thesis the modular architecture of the
platform has been presented, together with the results obtained for the solution
to the recognition problems related to the different biometrics and their possible
fusion. Finally, an analysis of the pattern recognition issues concerning the
area of videosurveillance has been discussed
Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery
A robust and fast automatic moving object detection and tracking system is
essential to characterize target object and extract spatial and temporal
information for different functionalities including video surveillance systems,
urban traffic monitoring and navigation, robotic. In this dissertation, I
present a collaborative Spatial Pyramid Context-aware moving object detection
and Tracking system. The proposed visual tracker is composed of one master
tracker that usually relies on visual object features and two auxiliary
trackers based on object temporal motion information that will be called
dynamically to assist master tracker. SPCT utilizes image spatial context at
different level to make the video tracking system resistant to occlusion,
background noise and improve target localization accuracy and robustness. We
chose a pre-selected seven-channel complementary features including RGB color,
intensity and spatial pyramid of HoG to encode object color, shape and spatial
layout information. We exploit integral histogram as building block to meet the
demands of real-time performance. A novel fast algorithm is presented to
accurately evaluate spatially weighted local histograms in constant time
complexity using an extension of the integral histogram method. Different
techniques are explored to efficiently compute integral histogram on GPU
architecture and applied for fast spatio-temporal median computations and 3D
face reconstruction texturing. We proposed a multi-component framework based on
semantic fusion of motion information with projected building footprint map to
significantly reduce the false alarm rate in urban scenes with many tall
structures. The experiments on extensive VOTC2016 benchmark dataset and aerial
video confirm that combining complementary tracking cues in an intelligent
fusion framework enables persistent tracking for Full Motion Video and Wide
Aerial Motion Imagery.Comment: PhD Dissertation (162 pages
Enhancing person annotation for personal photo management using content and context based technologies
Rapid technological growth and the decreasing cost of photo capture means that we are all taking more digital photographs than ever before. However, lack of technology for automatically organising personal photo archives has resulted in many users left with poorly annotated photos, causing them great frustration when such photo collections are to be browsed or searched at a later time. As a result, there has recently been significant research interest in technologies for supporting effective annotation.
This thesis addresses an important sub-problem of the broad annotation problem, namely "person annotation" associated with personal digital photo management. Solutions to this problem are provided using content analysis tools in combination with context data within the experimental photo management framework, called âMediAssistâ. Readily available image metadata, such as location and date/time, are captured from digital cameras with in-built GPS functionality, and thus provide knowledge about when and where the photos were taken. Such information is then used to identify the "real-world" events corresponding to certain activities in the photo capture process. The
problem of enabling effective person annotation is formulated in such a way that both "within-event" and "cross-event" relationships of persons' appearances are captured.
The research reported in the thesis is built upon a firm foundation of content-based analysis technologies, namely face detection, face recognition, and body-patch matching together with data fusion.
Two annotation models are investigated in this thesis, namely progressive and non-progressive. The effectiveness of each model is evaluated against varying proportions of
initial annotation, and the type of initial annotation based on individual and combined face, body-patch and person-context information sources. The results reported in the thesis strongly validate the use of multiple information sources for person annotation whilst
emphasising the advantage of event-based photo analysis in real-life photo management systems
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Video big data: an agile architecture for systematic exploration and analytics
Video is currently at the forefront of most business and natural environments. In surveillance, it is the most important technology as surveillance systems reveal information and patterns for solving many security problems including crime prevention. This research investigates technologies that currently drive video surveillance systems with a view to optimization and automated decision support.
The investigation reveals some features and properties that can be optimised to improve performance and derive further benefits from surveillance systems. These aspects include system-wide architecture, meta-data generation, meta-data persistence, object identification, object tagging, object tracking, search and querying sub-systems. The current less-than-optimum performance is attributable to many factors, which include massive volume, variety, and velocity (the speed at which streaming video transmit to storage) of video data in surveillance systems.
Research contributions are 2-fold. First, we propose a system-wide architecture for designing and implementing surveillance systems, based on the authorsâ system architecture for generating meta-data. Secondly, we design a simulation model of a multi-view surveillance system from which the researchers generate simulated video streams in large volumes. From each video sequence in the model, the authors extract meta-data and apply a novel algorithm for predicting the location of identifiable objects across a well-connected camera cluster.
This research provide evidence that independent surveillance systems (for example, security cameras) can be unified across a geographical location such as a smart city, where each network is administratively owned and managed independently. Our investigation involved 2 experiments - first, the implementation of a web-based solution where we developed a directory service for managing, cataloguing, and persisting metadata generated by the surveillance networks. The second experiment focused on the set up, configuration and the architecture of the surveillance system. These experiments involved the investigation and demonstration of 3 loosely coupled service-oriented APIs â these services provided the capability to generate the query-able metadata.
The results of our investigations provided answers to our research questions - the main question being âto what degree of accuracy can we predict the location of an object in a connected surveillance networkâ. Our experiment also provided evidence in support of our hypothesis â âit is feasible to âexploreâ unified surveillance data generated from independent surveillance networksâ
Principal Component Analysis
This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction