Abstract
In this paper we propose a classifier capable of recognizing human body static poses and body gestures in real time. The method is called the gesture description language (GDL). The proposed methodology is intuitive, easily thought and reusable for any kind of body gestures. The very heart of our approach is an automated reasoning module. It performs forward chaining reasoning (like a classic expert system) with its inference engine every time new portion of data arrives from the feature extraction library. All rules of the knowledge base are organized in GDL scripts having the form of text files that are parsed with a LALR-1 grammar. The main novelty of this paper is a complete description of our GDL script language, its validation on a large dataset (1,600 recorded movement sequences) and the presentation of its possible application. The recognition rate for examined gestures is within the range of 80.5–98.5 %. We have also implemented an application that uses our method: it is a three-dimensional desktop for visualizing 3D medical datasets that is controlled by gestures recognized by the GDL module.
Highlights
Most contemporary home and mobile computers are equipped with build-in cameras and video capture multimedia devices
In this paper we propose a classifier capable of recognizing human body static poses and body gestures in real time
The recognition rate for all the gestures ranged from 80.5 to 98.5 %. This allows multimedia applications that use our methodology to support the user in a convenient way
Summary
Most contemporary home and mobile computers are equipped with build-in cameras and video capture multimedia devices. There is a heavy demand for applications that utilize these sensors. One possible field of application is natural user interfaces (NI). The NI is a concept of human-device interaction based on human senses, mostly focused on hearing and vision. In the case of video data, NI allows user to interact with a computer by giving gesture- and pose-based commands. To recognize and interpret these instructions, proper classification methods have to be applied. The basic approach to gesture recognition is to formulate this problem as a time varying signals analysis. There are many approaches to complete this task. The choice of the optimal method depends on time sequence features we are dealing with
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