Compliant Navigation Mechanisms Utilizing Probabilistic Motion Patterns of Humans in a Camera Network

Motion trajectories provide rich spatio-temporal information about a person's activities. In this paper we first employ an algorithm for learning collections of these trajectories that characterize representative motion patterns of persons. Data recorded with a non-overlapping camera network is...

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Vydané v:Advanced robotics Ročník 22; číslo 9; s. 929 - 948
Hlavní autori: Liang, Zhiwei, Ma, Xudong, Dai, Xianzhong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: 01.09.2008
Predmet:
camera network
compliant navigation mechanisms
Fuzzy K-means algorithm
multi-camera tracking
probabilistic motion patterns
ISSN:0169-1864, 1568-5535
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Abstract Motion trajectories provide rich spatio-temporal information about a person's activities. In this paper we first employ an algorithm for learning collections of these trajectories that characterize representative motion patterns of persons. Data recorded with a non-overlapping camera network is clustered hierarchically using a fuzzy K-means algorithm based on spatial and temporal information, respectively, then each motion pattern is represented with a series of Gaussian distributions. Subsequently, a method is proposed to improve behaviors of a mobile robot according to moving intentions of people. In our approach, whenever the camera network detects a person it computes a probabilistic estimate about which motion pattern the person might be engaged in according to the learned models of people's motion behaviors. During path planning the robot then uses this prediction to adapt its navigation mechanisms. In practical experiments carried out on a real robot we demonstrate that our approach allows a robot to quickly adjust its navigation tactics according to the activities of people in an office environment.
AbstractList Motion trajectories provide rich spatio-temporal information about a person's activities. In this paper we first employ an algorithm for learning collections of these trajectories that characterize representative motion patterns of persons. Data recorded with a non-overlapping camera network is clustered hierarchically using a fuzzy K-means algorithm based on spatial and temporal information, respectively, then each motion pattern is represented with a series of Gaussian distributions. Subsequently, a method is proposed to improve behaviors of a mobile robot according to moving intentions of people. In our approach, whenever the camera network detects a person it computes a probabilistic estimate about which motion pattern the person might be engaged in according to the learned models of people's motion behaviors. During path planning the robot then uses this prediction to adapt its navigation mechanisms. In practical experiments carried out on a real robot we demonstrate that our approach allows a robot to quickly adjust its navigation tactics according to the activities of people in an office environment.
Author DAI Xianzhong
LIANG Zhiwei
MA Xudong
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Snippet Motion trajectories provide rich spatio-temporal information about a person's activities. In this paper we first employ an algorithm for learning collections...
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SubjectTerms camera network
compliant navigation mechanisms
Fuzzy K-means algorithm
multi-camera tracking
probabilistic motion patterns
Title Compliant Navigation Mechanisms Utilizing Probabilistic Motion Patterns of Humans in a Camera Network
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