C4.5: programs for machine learning
C4.5: programs for machine learning
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Visual Hull Concept for Silhouette-Based Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Divergence-Based Medial Surfaces
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Machine Learning
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
The i3DPost Multi-View and 3D Human Action/Interaction Database
CVMP '09 Proceedings of the 2009 Conference for Visual Media Production
Human activity analysis: A review
ACM Computing Surveys (CSUR)
3D Human Action Recognition for Multi-view Camera Systems
3DIMPVT '11 Proceedings of the 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission
Cross-view action recognition via view knowledge transfer
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Human action recognition is an important area of research in computer vision. Its applications include surveillance systems, patient monitoring, human-computer interaction, just to name a few. Numerous techniques have been developed to solve this problem in 2D and 3D spaces. However 3D imaging gained a lot of interest nowadays. In this paper we propose a novel view-independent action recognition algorithm based on fusion between a global feature and a graph based feature. We used the motion history of skeleton volumes; we compute a skeleton for each volume and a motion history for each action. Then, alignment is performed using cylindrical coordinates-based Fourier transform to form a feature vector. A dimension reduction step is subsequently applied using PCA and action classification is carried out by using Mahalonobis distance, and Linear Discernment analysis. The second feature is the temporal changes in bounding volume, volumes are aligned using PCA and each divided into sub volumes then temporal change in volume is calculated and classified using Logistic Model Trees. The fusion is done using majority vote. The proposed technique is evaluated on the benchmark IXMAS and i3DPost datasets where results of the fusion are compared against using each feature individually. Obtained results demonstrate that fusion improve the recognition accuracy over individual features and can be used to recognize human actions independent of view point and scale.