2013 | OriginalPaper | Buchkapitel
One-Shot Learning for Real-Time Action Recognition
verfasst von : Sean Ryan Fanello, Ilaria Gori, Giorgio Metta, Francesca Odone
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The goal of the paper is to develop a one-shot real-time learning and recognition system for 3D actions. We use RGBD images, combine motion and appearance cues, and map them into a new overcomplete space. The proposed method relies on descriptors based on 3D Histogram of Flow (3DHOF) and on Global Histogram of Oriented Gradient (GHOG); adaptive sparse coding (SC) is further applied to capture high-level patterns. We add effective on-line video segmentation and finally the recognition of actions through linear SVMs. The main contribution of the paper is a real-time system for one-shot action modeling; moreover we highlight the effectiveness of sparse coding techniques to represent 3D actions. We obtain very good results on the ChaLearn Gesture Dataset and with a Kinect sensor.