Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matrix algorithms
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
ACM SIGGRAPH 2005 Papers
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Function Space of an Activity
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time human action recognition by luminance field trajectory analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Distribution-Based Dimensionality Reduction Applied to Articulated Motion Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Action recognition in video by sparse representation on covariance manifolds of silhouette tunnels
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition
IEEE Transactions on Image Processing
Expandable Data-Driven Graphical Modeling of Human Actions Based on Salient Postures
IEEE Transactions on Circuits and Systems for Video Technology
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Silhouette-based human action recognition using sequences of key poses
Pattern Recognition Letters
Hi-index | 0.00 |
Manifold learning is an efficient approach for recognizing human actions. Most of the previous embedding methods are learned based on the distances between frames as data points. Thus they may be efficient in the frame recognition framework, but they will not guarantee to give optimum results when sequences are to be classified as in the case of action recognition in which temporal constraints convey important information. In the sequence recognition framework, sequences are compared based on the distances defined between sets of points. Among them Spatio-temporal Correlation Distance (SCD) is an efficient measure for comparing ordered sequences. In this paper we propose a novel embedding which is optimum in the sequence recognition framework based on SCD as the distance measure. Specifically, the proposed embedding minimizes the sum of the distances between intra-class sequences while seeking to maximize the sum of distances between inter-class points. Action sequences are represented by key poses chosen equidistantly from one action period. The action period is computed by a modified correlation-based method. Action recognition is achieved by comparing the projected sequences in the low-dimensional subspace using SCD or Hausdorff distance in a nearest neighbor framework. Several experiments are carried out on three popular datasets. The method is shown not only to classify the actions efficiently obtaining results comparable to the state of the art on all datasets, but also to be robust to additive noise and tolerant to occlusion, deformation and change in view point. Moreover, the method outperforms other classical dimension reduction techniques and performs faster by choosing less number of postures.