A new approach to video sequence recognition based on statistical methods

A fast method for image sequence recognition is presented. The method is based on a discrete statistical model consisting of a vector quantizer and a special probabilistic neural network, which allows one to classify image sequences without applying rules depending on the content of the sequence. The simple feature extraction also allows the classification with discrete hidden Markov models. As an application we present results from a test conducted for the classification of various gestures done by human beings in front of a video camera. For both classification methods we obtained promising recognition results in real time. The system obtained 90.0% recognition rate for person-independent classification of 10 or even 15 different gestures, which we considered as a surprisingly high rate for such a complex task. The system has been demonstrated at a large industrial fair and has confirmed its high recognition rates and its robustness under real-world conditions.