Understanding purposeful human motion

Wren, Christopher R.; Clarkson, Brian; Pentland, Alex

doi:10.1109/afgr.2000.840662

Cited by 56 publications

(29 citation statements)

References 8 publications

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“…An alphabet consisting of motion-entities would make computer vision-based human motion capture much easier, since it will transform the pose estimation problem into a recognition problem, i.e., recognize a sequence of symbols. This has already happened in Bregler [17] where the letters are called movemes, and Wren and Pentland [143] where the letters are called behaviors. Even though their alphabets are rather limited it is still a step in a very interesting direction.…”

Section: Future Directionsmentioning

confidence: 99%

“…An HMM is used to classify three different gait categories: running, walking, and skipping. This type of high level symbolic representation is also used in the work by Wren et al [143]. They automatically build a behavior alphabet (a behavior is similar to a moveme) and model each behavior using an HMM.…”

Section: Dynamic Recognitionmentioning

confidence: 99%

“…Due to the high dimensionality of the problem an analytic solution is not possible and a numeric iterative approach is generally used [139]. An alternative approach is to predict just one state and let the difference between the synthesized data and the measurements be used as an error signal to correct the state of the model [47,146]. An excellent framework for this type of approach is the Kalman filter.…”

Section: Direct Model Usementioning

confidence: 99%

“…In the work by Wren and Pentland [144][145][146] blobs are used as the abstraction level. The Pfinder algorithm [142] (see Section 4) is run on two images from two different cameras producing 3D blobs for the hands and the head.…”

Section: Abstraction Levelsmentioning

confidence: 99%

“…As an example of state of the art we consider the work by Wren et al [143]. First of all they use the Pfinder algorithm [142] as the underlying tracking methods.…”

mentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Computer Vision-Based Human Motion Capture

Moeslund¹,

Granum²

2001

Computer Vision and Image Understanding

1,422

642

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Section: Future Directionsmentioning

confidence: 99%

Section: Dynamic Recognitionmentioning

confidence: 99%

Section: Direct Model Usementioning

confidence: 99%

Section: Abstraction Levelsmentioning

confidence: 99%

“…As an example of state of the art we consider the work by Wren et al [143]. First of all they use the Pfinder algorithm [142] as the underlying tracking methods.…”

mentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Computer Vision-Based Human Motion Capture

Moeslund¹,

Granum²

2001

Computer Vision and Image Understanding

1,422

642

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Pattern recognition: Historical perspective and future directions

Rosenfeld

Wechsler

2000

Int. J. Imaging Syst. Technol.

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What being walks sometimes on two feet, sometimes on three, and sometimes on four, and is weakest when it has the most?" -The Sphinx's Riddle ABSTRACT: Pattern recognition is one of the most important functionalities for intelligent behavior and is displayed by both biological and artificial systems. Pattern recognition systems have four major components: data acquisition and collection, feature extraction and representation, similarity detection and pattern classifier design, and performance evaluation. In addition, pattern recognition systems are successful to the extent that they can continuously adapt and learn from examples; the underlying framework for building such systems is predictive learning. The pattern recognition problem is a special case of the more general problem of statistical regression; it seeks an approximating function that minimizes the probability of misclassification. In this framework, data representation requires the specification of a basis set of approximating functions. Classification requires an inductive principle to design and model the classifier and an optimization or learning procedure for classifier parameter estimation. Pattern recognition also involves categorization: making sense of patterns not previously seen. The sections of this paper deal with the categorization and functional approximation problems; the four components of a pattern recognition system; and trends in predictive learning, feature selection using "natural" bases, and the use of mixtures of experts in classification.

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Interactive Multi-Modal Robot Programming

Iba

Paredis

Khosla

2006

Springer Tracts in Advanced Robotics

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The goal of the Interactive Multi-Modal Robot Programming system is a comprehensive human-machine interface that allows non-experts to compose robot programs conveniently. Two key characteristics of this novel programming approach are that the user can provide feedback interactively at any time through an intuitive interface and that the system infers the user's intent to support interaction. The framework takes a three-step approach to the problem: multi-modal recognition, intention interpretation, and prioritized task execution. The system is demonstrated by interactively controlling and programming a mobile vacuum cleaning robot. The demonstrations are used to exemplify the interactive programming and plan recognition aspects of the research.

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Understanding purposeful human motion

Cited by 56 publications

References 8 publications

A Survey of Computer Vision-Based Human Motion Capture

A Survey of Computer Vision-Based Human Motion Capture

Pattern recognition: Historical perspective and future directions

Interactive Multi-Modal Robot Programming

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