Authors:
Baptiste Lemarcis
1
;
Valère Plantevin
1
;
Bruno Bouchard
2
and
Bob-Antoine-Jerry Ménélas
1
Affiliations:
1
Université du Québec à Chicoutimi (UQAC), Canada
;
2
IEEE Senior, Canada
Keyword(s):
Online Gesture Recognition, Streaming, Template Matching Method, LCSS, LM-WLCSS.
Abstract:
In this paper, we present and evaluate a new algorithm for online gesture recognition in noisy streams. This
technique relies upon the proposed LM-WLCSS (Limited Memory and Warping LCSS) algorithm that has
demonstrated its efficiency on gesture recognition. This new method involves a quantization step (via the KMeans
clustering algorithm). This transforms new data to a finite set. In this way, each new sample can be
compared to several templates (one per class) and gestures are rejected based on a previously trained rejection
threshold. Then, an algorithm, called SearchMax, find a local maximum within a sliding window and output
whether or not the gesture has been recognized. In order to resolve conflicts that may occur, another classifier
could be completed. As the K-Means clustering algorithm, needs to be initialized with the number of clusters
to create, we also introduce a straightforward optimization process. Such an operation also optimizes the
window size for the SearchMax alg
orithm. In order to demonstrate the robustness of our algorithm, an experiment
has been performed over two different data sets. However, results on tested data sets are only accurate
when training data are used as test data. This may be due to the fact that the method is in an overlearning state.
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