The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Automatic temporal segment detection and affect recognition from face and body display
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust 3d action recognition with random occupancy patterns
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Robust 3d action recognition with random occupancy patterns
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Online RGB-D gesture recognition with extreme learning machines
Proceedings of the 15th ACM on International conference on multimodal interaction
Evolutionary joint selection to improve human action recognition with RGB-D devices
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
Depth sensor assisted real-time gesture recognition for interactive presentation
Journal of Visual Communication and Image Representation
Effective 3D action recognition using EigenJoints
Journal of Visual Communication and Image Representation
Pose-based human action recognition via sparse representation in dissimilarity space
Journal of Visual Communication and Image Representation
Human activity recognition using multi-features and multiple kernel learning
Pattern Recognition
Online gesture recognition from pose kernel learning and decision forests
Pattern Recognition Letters
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In this paper, we propose an effective method to recognize human actions from sequences of depth maps, which provide additional body shape and motion information for action recognition. In our approach, we project depth maps onto three orthogonal planes and accumulate global activities through entire video sequences to generate the Depth Motion Maps (DMM). Histograms of Oriented Gradients (HOG) are then computed from DMM as the representation of an action video. The recognition results on Microsoft Research (MSR) Action3D dataset show that our approach significantly outperforms the state-of-the-art methods, although our representation is much more compact. In addition, we investigate how many frames are required in our framework to recognize actions on the MSR Action3D dataset. We observe that a short sub-sequence of 30-35 frames is sufficient to achieve comparable results to that operating on entire video sequences.