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Visual Categorization of Children and Adult Walking Styles

  • James W. Davis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2091)

Abstract

We present an approach for visual discrimination of children from adults in video using characteristic regularities present in their locomotion patterns. The framework employs computer vision to analyze correlated, scale invariant locomotion properties for classifying different styles of walking. Male and female subjects for the experiments include six children (3–5 yrs) and nine adults (30–52 yrs). For the analysis, we coordinate a minimalist point-representation of the human body with a space-time analysis of head and ankle trajectories to characterize the modality. Together the properties of relative stride length and stride frequency are shown to clearly differentiate children from adult walkers. The highly correlated log-linear relationships for the stride properties are exploited to reduce the categorization problem to a linear discrimination task. Using a trained two-class linear perceptron, we were able to achieve a correct classification rate of 93-95% on our dataset. Our approach emphasizing the natural modal behavior in human motion offers a useful and general methodology as the basis for designing efficient motion recognition systems using limited visual features.

Keywords

Stride Length Visual Categorization Stride Frequency Computer Vision System Human Locomotion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • James W. Davis
    • 1
  1. 1.Dept. of Computer and Information ScienceMotion Recognition LaboratoryColumbusUSA

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