Segmentation and Recognition Model for Complex Action Sequences

Abstract

The researches on human behavior recognition have always on the trend, amongst which the sensor-based recognition is one of the most important fields.While the performance of individual action recognition methods can be described as satisfactory, however, it is difficult to obtain the location and category information from continuous and complex action sequences. In this paper we proposed a novel segmentation and recognition model for continuous action sequence. This model segments the sequence then categorize them into three types: dynamic actions, static actions and transition actions. By analyzing the characteristics of gait signals in dynamic actions, we proposed a method of biometric-based gait cycle detection to identify all the dynamic actions in the sequence. Then, based on the difference of the signal conversion frequency between static actions and transition actions, we analyzed the fluctuation point distribution in this two kinds of actions. The result shows that introducing the threshold detection algorithm can be of great benefits when distinguishing static actions from transition actions. This model was tested with public data set and the experiments have proved that the model achieves excellent results on the complex continuous action data set disclosed by UCI. We reached the accuracy over 98%, which showed our model is better than any known method.