Two Methods of Gesture Recognition

In this paper I will review two recent approaches to gesture recognition. First, I will describe the Condensation algorithm [2] and its extensions to gesture recognition [3] [1]. Then, I will describe a method that uses multi-scale motion segmentation to monitor motion over time and a Time Delayed Neural Network to match this motion to gestures [4]. My focus will be on how these algorithms use models of motion over time to classify gestures. Finally, I’ll summarize a few of the comparative advantages of each algorithm.

[1]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[2]  Narendra Ahuja,et al.  Recognizing hand gesture using motion trajectories , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[3]  Michael J. Black,et al.  A Probabilistic Framework for Matching Temporal Trajectories: CONDENSATION-Based Recognition of Gestures and Expressions , 1998, ECCV.

[4]  Michael Isard,et al.  A mixed-state condensation tracker with automatic model-switching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).