We presented an approach for the visual discrimination of children (3-5 years old) from adults using stride-based properties of their walking style. Trajectories of marked head and ankle positions for six children and nine adults were used to compute the relative stride and stride frequency for each walker at di?erent speeds. The distinction between child and adult for these features is quite strong and reduces the task of categorization to a linear discrimination test. Using a trained two-class linear perceptron, we were ...
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We presented an approach for the visual discrimination of children (3-5 years old) from adults using stride-based properties of their walking style. Trajectories of marked head and ankle positions for six children and nine adults were used to compute the relative stride and stride frequency for each walker at di?erent speeds. The distinction between child and adult for these features is quite strong and reduces the task of categorization to a linear discrimination test. Using a trained two-class linear perceptron, we were able to achieve a correct classi?- tion rate of 93-95% for our dataset. Given that only two motion features were used to characterize and di?erentiate children from adults, the result is quite - couraging. The use of natural modes as a means of visual categorization provides a useful bottom-up framework for the classi?cation and recognition of humans in motion. References 1. A. Baumberg and D. Hogg. Learning ?exible models from image sequences. In Proc. European Conf. Comp. Vis., pages 299-308, 1994. 2. W. Boda, W. Tapp, and T. Findley. Biomechanical comparison of treadmill and overground walking. In Proc. Can. Soc. for Biomech., pages 88-89, 1994. 3. C. Bregler and J. Malik. Tracking people with twists and exponential maps. In Proc. Comp. Vis. and Pattern Rec., pages 8-15, 1998. 4. I. Chang and C. Huang. The model-based human body motion analysis system. Image and Vision Comp., 18(14):1067-1083, 2000. 5. D. Gavrila. Pedestrian detection from a moving vehicle. In Proc. European Conf.
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