Skeletonization in a real-time gesture recognition system

Advances in technology continue to make both hardware and software affordable and accessible; we have seen a rapid growth in computer vision and image processing applications. One area of interest in vision and image processing is automated identification of objects in real-time or recorded video streams and analysis of these identified objects. An important topic of research in this context is identification of humans and interpreting their actions. For example, a camera mounted on the front of a vehicle can capture images of pedestrians and analyse their actions to interpret if they are about to cross the path of the oncoming vehicle. This could be an important accident prevention technique. This paper presents part of our work in a project that deals with object detection and gesture recognition on video streams in real time that could support such applications. In order to recognize gestures of humans and human movements, we must first identify the moving objects in a video stream. Then the identified objects need to be tracked over the frames of the video and classified as human or non-human. Thereafter, the human objects must be skeletonized in order to encode their movements before interpretation can be done. This paper presents a research and analysis of various skeletonizing methods and illustrates our selection of a particular skeletonization method through implementation of algorithms and analysis of experimental data.