4,075 research outputs found
A query description model based on basic semantic unit composite Petri-Net for soccer video
Digital video networks are making available increasing amounts of sports video data. The volume of material on offer means that sports fans often rely on prepared summaries of game highlights to follow the progress of their favourite teams. A significant application area for automated video analysis technology is the generation of personalized highlights of sports events. One of the most popular sports around world is soccer. A soccer game is composed of a range of significant events, such as goal scoring, fouls, and substitutions. Automatically detecting these events in a soccer video can enable users to interactively design their own highlights programmes. From an analysis of broadcast soccer video, we propose a query description model based on Basic Semantic Unit Composite Petri-Nets (BSUCPN) to automatically detect significant events within soccer video. Firstly we define a Basic Semantic Unit (BSU) set for soccer videos based on identifiable feature elements within a soccer video, Secondly we design Composite Petri-Net (CPN) models for semantic queries and use these to describe BSUCPNs for semantic events in soccer videos. A particular strength of this approach is that users are able to design their own semantic event queries based on BSUCPNs to search interactively within soccer videos. Experimental results
based on recorded soccer broadcasts are used to illustrate the potential of this approach
Modeling Concurrency in Parallel Debugging
We propose a description language, Data Path Expressions (DPEs), for modeling the behavior of parallel programs. We have designed DPEs as a high-level debugging language, where the debugging paradigm is for the programmer to describe the expected program behavior and for the debugger to compare the actual program behavior during execution to detect program errors. We classify DPEs into five subclasses according to syntactic criteria, and characterize their semantics in terms of a hierarchy of extended Petri Net models. The characterization demonstrates the power of DPEs for modeling (true) concurrency. We also present predecessor automata as a mechanism for implementing the third subclass of DPEs, which expresses bounded parallelism. Predecessor automata extend finite state automata to recognize or generate partial ordering graphs as well as strings, and provide efficient event recognizers for parallel debugging. We briefly describe the application of DPEs race conditions, deadlock and starvation
Integration and coordination in a cognitive vision system
In this paper, we present a case study that exemplifies
general ideas of system integration and coordination.
The application field of assistant technology provides an
ideal test bed for complex computer vision systems including
real-time components, human-computer interaction, dynamic
3-d environments, and information retrieval aspects.
In our scenario the user is wearing an augmented reality device
that supports her/him in everyday tasks by presenting
information that is triggered by perceptual and contextual
cues. The system integrates a wide variety of visual functions
like localization, object tracking and recognition, action
recognition, interactive object learning, etc. We show
how different kinds of system behavior are realized using
the Active Memory Infrastructure that provides the technical
basis for distributed computation and a data- and eventdriven
integration approach
Parallel symbolic state-space exploration is difficult, but what is the alternative?
State-space exploration is an essential step in many modeling and analysis
problems. Its goal is to find the states reachable from the initial state of a
discrete-state model described. The state space can used to answer important
questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a
starting point for sophisticated investigations expressed in temporal logic.
Unfortunately, the state space is often so large that ordinary explicit data
structures and sequential algorithms cannot cope, prompting the exploration of
(1) parallel approaches using multiple processors, from simple workstation
networks to shared-memory supercomputers, to satisfy large memory and runtime
requirements and (2) symbolic approaches using decision diagrams to encode the
large structured sets and relations manipulated during state-space generation.
Both approaches have merits and limitations. Parallel explicit state-space
generation is challenging, but almost linear speedup can be achieved; however,
the analysis is ultimately limited by the memory and processors available.
Symbolic methods are a heuristic that can efficiently encode many, but not all,
functions over a structured and exponentially large domain; here the pitfalls
are subtler: their performance varies widely depending on the class of decision
diagram chosen, the state variable order, and obscure algorithmic parameters.
As symbolic approaches are often much more efficient than explicit ones for
many practical models, we argue for the need to parallelize symbolic
state-space generation algorithms, so that we can realize the advantage of both
approaches. This is a challenging endeavor, as the most efficient symbolic
algorithm, Saturation, is inherently sequential. We conclude by discussing
challenges, efforts, and promising directions toward this goal
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