Addressing the semantic gap between video sensors and applications

In this paper, we propose a framework to support the bridging of applications and computer-vision based sensor networks. We argue that the semantic gap, the difference between the data collected in a sensor network and the information needed by the application, in video-based sensor networks can only be addressed by providing systems support in such a way that allows users and computing systems to meet in the middle. We first outline the vision of the system that we are working towards. We then describe initial experiments that we have conducted using a functional component of the system applied to real-world video data that is being collected by intelligent transportation systems researchers.