23,351 research outputs found
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions
The focus of this paper is on solving multi-robot planning problems in
continuous spaces with partial observability. Decentralized partially
observable Markov decision processes (Dec-POMDPs) are general models for
multi-robot coordination problems, but representing and solving Dec-POMDPs is
often intractable for large problems. To allow for a high-level representation
that is natural for multi-robot problems and scalable to large discrete and
continuous problems, this paper extends the Dec-POMDP model to the
decentralized partially observable semi-Markov decision process (Dec-POSMDP).
The Dec-POSMDP formulation allows asynchronous decision-making by the robots,
which is crucial in multi-robot domains. We also present an algorithm for
solving this Dec-POSMDP which is much more scalable than previous methods since
it can incorporate closed-loop belief space macro-actions in planning. These
macro-actions are automatically constructed to produce robust solutions. The
proposed method's performance is evaluated on a complex multi-robot package
delivery problem under uncertainty, showing that our approach can naturally
represent multi-robot problems and provide high-quality solutions for
large-scale problems
Severity-sensitive norm-governed multi-agent planning
This research was funded by Selex ES. The software developed during this research, including the norm analysis and planning algorithms, the simulator and harbour protection scenario used during evaluation is freely available from doi:10.5258/SOTON/D0139Peer reviewedPublisher PD
- ā¦