The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Categorization of Children and Adult Walking Styles
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Hand motion gestural oscillations and multimodal discourse
Proceedings of the 5th international conference on Multimodal interfaces
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
View-invariant analysis of periodic motion
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Performing content-based retrieval of humans using gait biometrics
Multimedia Tools and Applications
3D Reconstruction of Periodic Motion from a Single View
International Journal of Computer Vision
Advances in automatic gait recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Reconstructing and analyzing periodic human motion from stationary monocular views
Computer Vision and Image Understanding
| Hi-index | 0.00 |
Our goal is to describe motion of a moving human figure in order to recognize individuals by variation in the characteristics of the motion description. We begin with a short sequence of images of a moving figure, taken by a static camera, and derive dense optical flow data for the sequence. We determine a range of scale-independent features of each how image as a whole, ranging from the motion of the centroid of the moving points (assuming a static background), to the integral of the torque relative to the centroid. We then analyze the periodic structure of these sequences. All elements are multiples of the fundamental period of the gait, but they differ in phase. The phase is time-invariant, since it is independent of the sampling period. We show that there are several regularities in the phase differences of the signals. Moreover, some scalar measures of the signals may be useful in recognition. The representation is model-free, and therefore could be used to characterize the motion of other non-rigid bodies.