4 January 2013 Conditional random field-based gesture recognition with depth information
Author Affiliations +
Gesture recognition is useful for human-computer interaction. The difficulty of gesture recognition is that instances of gestures vary both in motion and shape in three-dimensional (3-D) space. We use depth information generated using Microsoft’s Kinect in order to detect 3-D human body components and apply a threshold model with a conditional random field in order to recognize meaningful gestures using continuous motion information. Body gesture recognition is achieved through a framework consisting of two steps. First, a human subject is described by a set of features, encoding the angular relationship between body components in 3-D space. Second, a feature vector is recognized using a threshold model with a conditional random field. In order to show the performance of the proposed method, we use a public data set, the Microsoft Research Cambridge-12 Kinect gesture database. The experimental results demonstrate that the proposed method can efficiently and effectively recognize body gestures automatically.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Hyunsook Chung, Hyunsook Chung, Hee-Deok Yang, Hee-Deok Yang, } "Conditional random field-based gesture recognition with depth information," Optical Engineering 52(1), 017201 (4 January 2013). https://doi.org/10.1117/1.OE.52.1.017201 . Submission:

Back to Top