The algorithm presented in this paper is designed to detect and track human movement in real-time from 3D footage. Techniques are discussed that hold potential for a tracking system when combined with stereoscopic video capture using the extra depth included in the footage. This information allows for the production of a robust and reliable system.
One of the major issues associated with this problem is computational expense. Real-time tracking systems have been designed that work off a single image for tasks such as video surveillance. The different images will be recorded as if they are eyes, at the same distance apart from one another approximately 6-7cm. In order to use 3D imagery two separate images need to be analysed, combined and the human motion detected and extracted. The greatest benefit of this system would be the extra image of information to which conventional systems do not have access such as the benefit of depth perception in the overlapping field of view from the cameras.
In this paper, we describe the motivation behind using 3D footage and the technical complexity of the problem. The system is shown tracking a human in a scene indoors and outdoors with video output from the system of the detected regions. This first prototype created here has further uses in the field of motion capture, computer gaming and augmented reality.
Linear Logic Based Calculi for Object Petri Nets
The theory of Petri nets has evolved in the past decades and has proved to be of great value in various fields of software development. Since the original net formalism emerged from Carl-Adam Petri’s work on communication and automata ([Pet62]) in the nineteen-sixties many flavours of extensions have been developed. Amongst these are predicate/transition nets ([GL79]), coloured Petri nets ([Jen79],[Jen80],[Jen81a],[Jen81b]), and hierarchical Petri nets ([Feh90],[Feh91],[Feh92],[Feh93],[HJS89], [HJS91]), to name but the most prominent examples of high-level nets. The emergence of object-oriented modular techniques in software design has only recently had a great impact on further extensions to the standard models of high-level Petri nets.
Petri nets are being used successfully for the specification and analysis of a diverse range of problems. The theory of Petri nets has had direct impact on computing and information sciences but the scope in which applications of the theory can be found is not limited to these. In fact there are many other disciplines that are influenced by Petri net theory in some way or another.
The design and analysis of parallel and distributed algorithms has benefited a lot from the research in Petri nets. The basic model of place/transition nets is well-studied, so that there is a wealth of results and techniques available. More recent development has shown that this basic model — even though it is well-understood — is lacking in support essential for the design of complex systems. This has led to some con- servative extensions of the basic model. Further high-level concepts have been added since, so that we are faced by an overwhelming diversity of net concepts.
The other main ingredient in this thesis is Linear Logic. Introduced in 1987 ([Gir87]) it has won a small but steadily growing group of admirers over the past decade, partly due to the beauty of the formalism and partly due to its usefulness in a variety of fields. Applications of Linear Logic can be found in areas like proof theory — its origin —, linguistics (e.g. [Hod92]), category theory (e.g. [See89], [Bar91]), and the theory of computation (e.g. [Abr93], [Kan94]), to name but a few.
Linear Logic has a close resemblance to Petri nets in that it has connectives that can handle resources in the same manner as ordinary Petri nets do. In a simplistic attempt to characterize Linear Logic, it can be described as allowing arguments over multisets, while classical logic ar- guments range over sets.
We try to mingle the two formalisms, defining Linear Logic Petri nets as a new formalism, allowing the study of different object Petri net formalisms in a uniform framework.
The PhD thesis of Dr Andy Weller, entitled “The Semi-Automated Classification of Sedimentary Organic Matter and Dinoflagellate Cysts in Palynological Preparations”.
The PhD Thesis of Dr Samuel Kemp, entitled “Gamma Test Analysis Tools For Non-Linear Time Series”.
The PhD Thesis of Dr Paul Jarvis, entitled “Determining Geographical Causal Relationships through the Development of Spatial Cluster Detection and Feature Selection Techniques”.
The PhD Thesis of Dr Ian Wilson, entitled “The Application of Artificial Intelligence Techniques to the Deep-Sea Container-Ship Cargo Stowage Problem”.