Making large-scale support vector machine learning practical
Advances in kernel methods
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Silhouette-Based Human Identification from Body Shape and Gait
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Synchronization of oscillations for machine perception of gaits
Computer Vision and Image Understanding
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait recognition using image self-similarity
EURASIP Journal on Applied Signal Processing
Gait shape estimation for identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
What image information is important in silhouette-based gait recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Extracting salient points and parts of shapes using modified kd-trees
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Robust Gait Recognition by Learning and Exploiting Sub-gait Characteristics
International Journal of Computer Vision
| Hi-index | 0.00 |
Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. Understanding the detection of unusual movement patterns as a two-class problem suggests using support vector machines for classification. We present a homeomorphisms between 2D lattices and binary shapes that provides a robust vector space embedding of segmented body silhouettes. Experimental results demonstrate that feature vectors obtained from this scheme are well suited to detect abnormal gait. Wavering, faltering, and falling can be detected reliably across individuals without tracking or recognising limbs or body parts.