Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
Gait Sequence Analysis Using Frieze Patterns
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
EigenGait: Motion-Based Recognition of People Using Image Self-Similarity
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
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Finding Periodicity in Space and Time
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical feature fusion for gait-based human recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3D tracking for gait characterization and recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Human gait recognition using temporal slices
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
| Hi-index | 0.00 |
This paper describes a new method to track people walking by matching their gait image sequences in the frequency domain. When a person walks at a distance from a camera, that person often appears and disappears due to being occluded by other people and/or objects, or by going out of the field of view. Therefore, it is important to track the person by taking correspondence of the image sequences between before and after the disappearance. In the case of tracking, the computational time is more crucial factor than that in the case of identification. We create a three-dimensional volume by piling up an image sequence of human walking. After using Fourier analysis to extract the frequency characteristics of the volume, our method computes the similarity of two volumes. We propose a method to compute their correlation of the amplitude of the principal frequencies to improve the cost of comparison. Finally, we experimentally test our method and validate that the amplitude of principal frequencies and spatial information are important to discriminate gait image sequences.