Visual gesture recognition is one of the main areas of research in human-computer and human-robot interfaces. In this paper we present a novel visual gesture recognition method for aircraft marshalling signals. To capture hand motion information, we used a color-based tracking algorithm with an adaptive window for each frame. A feature selection algorithm was used to classify the motion information into four different gesture phases. By using the gesture phase information, we built the gesture model, which consisted of a symbol sequence and a number of probabilities. Each gesture model was learned from the longest common subsequence (LCS) of a set of symbol sequences and the probability of the symbols. A similarity measure using the proposed gesture model is presented that combines the deterministic and probabilistic matching scores. In the experiments we show the efficiency and accuracy of the proposed method.
Visual recognition of aircraft marshalling signals using gesture phase analysis
2008 IEEE Intelligent Vehicles Symposium ; 853-858
2008-06-01
339738 byte
Conference paper
Electronic Resource
English
Visual Recognition of Aircraft Marshalling Signals Using Gesture Phase Analysis
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