Establishing motion correspondence
CVGIP: Image Understanding
Towards model-based recognition of human movements in image sequences
CVGIP: Image Understanding
Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Describing motion for recognition
ISCV '95 Proceedings of the International Symposium on Computer Vision
View-invariant Estimation of Height and Stride for Gait Recognition
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Synchronization of oscillations for machine perception of gaits
Computer Vision and Image Understanding
An adaptive focus-of-attention model for video surveillance and monitoring
Machine Vision and Applications
Gait recognition using image self-similarity
EURASIP Journal on Applied Signal Processing
Synchronization of oscillations for machine perception of gaits
Computer Vision and Image Understanding
Minimal-latency human action recognition using reliable-inference
Image and Vision Computing
Gait analysis of gender and age using a large-scale multi-view gait database
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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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.