K-means clustering via principal component analysis
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
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
Posture and Gesture Recognition using 3D Body Shapes Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
A differential geometric approach to representing the human actions
Computer Vision and Image Understanding
Action Signature: A Novel Holistic Representation for Action Recognition
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
HOG-based descriptors on rotation invariant human detection
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Hand posture recognition with multiview descriptors
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
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We introduce a new HoG (Histogram of Oriented Gradients) tracker for Gesture Recognition. Our main contribution is to build HoG trajectory descriptors (representing local motion) which are used for gesture recognition. First,we select for each individual in the scene a set of corner points to determine textured regions where to compute 2DHoG descriptors. Second, we track these 2D HoG descriptors in order to build temporal HoG descriptors. Lost descriptors are replaced by newly detected ones. Finally, we extract the local motion descriptors to learn offline a set of given gestures.Then, a new video can be classified according to the gesture occurring in the video. Results shows that the tracker performs well compared to KLT tracker [1]. The generated local motion descriptors are validated through gesture learning-classification using the KTH action database [2].