Improvement of Continuous Dynamic Programming for Human Gesture Recognition
Haiyuan WU, Ryutaro KIDO, Tadayoshi SHIOYAMA
This paper describes an improved Continuous Dynamic Programming (CDP) for human gesture recognition. In order to cope with the variation in the amplitude of the input signal, a reference pattern set containing patterns with different mean amplitude is used instead of a single reference pattern. Furthermore, a new multiple search path determination strategy is developed in place of the traditional single one to deal with the variation in the speed of the input gesture. Also, a matching error controlled path determination method is newly introduced to reduce the influence of noise. The experimental results show this improved CDP can give not only stable identification results compared with the conventional one, but also the degree of the size and speed of the gesture to be distinguished.