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Accurate and Efficient Gesture Spotting via Pruning and Subgesture Reasoning
 

Summary: Accurate and Efficient Gesture Spotting via
Pruning and Subgesture Reasoning
Jonathan Alon, Vassilis Athitsos, and Stan Sclaroff
Computer Science Department
Boston University
Boston, MA 02215, USA
Abstract. Gesture spotting is the challenging task of locating the start
and end frames of the video stream that correspond to a gesture of inter-
est, while at the same time rejecting non-gesture motion patterns. This
paper proposes a new gesture spotting and recognition algorithm that is
based on the continuous dynamic programming (CDP) algorithm, and
runs in real-time. To make gesture spotting efficient a pruning method
is proposed that allows the system to evaluate a relatively small num-
ber of hypotheses compared to CDP. Pruning is implemented by a set
of model-dependent classifiers, that are learned from training examples.
To make gesture spotting more accurate a subgesture reasoning process
is proposed that models the fact that some gesture models can falsely
match parts of other longer gestures. In our experiments, the proposed
method with pruning and subgesture modeling is an order of magnitude
faster and 18% more accurate compared to the original CDP algorithm.

  

Source: Athitsos, Vassilis - Department of Computer Science and Engineering, University of Texas at Arlington

 

Collections: Computer Technologies and Information Sciences