Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
Human motion analysis: a review
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
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
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Nonlinear Generative Models for Dynamic Shape and Dynamic Appearance
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Human motion recognition using an eigenspace
Pattern Recognition Letters
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
| Hi-index | 0.00 |
This paper addresses the problem of classifying actions performed by a human subject in a video sequence. A representation eigenspace approach based on the visual appearance is used to train the classifier. Before dimensionality reduction exploiting the PCA/LLE algorithms, a high dimensional description of each frame of the video sequence is constructed, based on foreground blob analysis. The classification task is performed by matching incrementally the reduced representation of the test image sequence against each of the learned ones, and accumulating matching scores until a decision is obtained; to this aim, two different metrics are introduced and evaluated. Experimental results demonstrate that the approach is accurate enough and feasible for behavior classification. Furthermore, we argue that the choice of both the feature descriptor and the metric for the matching score can dramatically influence the performance of the results.