114,186 research outputs found
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?
Goal recognition is an important problem in many application domains (e.g.,
pervasive computing, intrusion detection, computer games, etc.). In many
application scenarios, it is important that goal recognition algorithms can
recognize goals of an observed agent as fast as possible. However, many early
approaches in the area of Plan Recognition As Planning, require quite large
amounts of computation time to calculate a solution. Mainly to address this
issue, recently, Pereira et al. developed an approach that is based on planning
landmarks and is much more computationally efficient than previous approaches.
However, the approach, as proposed by Pereira et al., also uses trivial
landmarks (i.e., facts that are part of the initial state and goal description
are landmarks by definition). In this paper, we show that it does not provide
any benefit to use landmarks that are part of the initial state in a planning
landmark based goal recognition approach. The empirical results show that
omitting initial state landmarks for goal recognition improves goal recognition
performance.Comment: Will be presented at KI 202
Domain independent goal recognition
Goal recognition is generally considered to follow plan recognition. The plan recognition problem is typically deďŹned to be that of identifying which plan in a given library of plans is being executed, given a sequence of observed actions. Once a plan has been identiďŹed, the goal of the plan can be assumed to follow. In this work, we address the problem of goal recognition directly, without assuming a plan library. Instead, we start with a domain description, just as is used for plan construction, and a sequence of action observations. The task, then, is to identify which possible goal state is the ultimate destination of the trajectory being observed. We present a formalisation of the problem and motivate its interest, before describing some simplifying assumptions we have made to arrive at a ďŹrst implementation of a goal recognition system, AUTOGRAPH. We discuss the techniques employed in AUTOGRAPH to arrive at a tractable approximation of the goal recognition problem and show results for the system we have implemented
3D Face Tracking and Texture Fusion in the Wild
We present a fully automatic approach to real-time 3D face reconstruction
from monocular in-the-wild videos. With the use of a cascaded-regressor based
face tracking and a 3D Morphable Face Model shape fitting, we obtain a
semi-dense 3D face shape. We further use the texture information from multiple
frames to build a holistic 3D face representation from the video frames. Our
system is able to capture facial expressions and does not require any
person-specific training. We demonstrate the robustness of our approach on the
challenging 300 Videos in the Wild (300-VW) dataset. Our real-time fitting
framework is available as an open source library at http://4dface.org
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