Target tracking and localization using infrared video imagery

One of the major problems in visually tracking objects is the need for human interpretation of the data. For instance, consider a person being tracked using video images. When this person hides behind an obstruction, and is therefore no longer visible for the camera, conventional tracking systems quickly lose track of the person and are no longer able to indicate where the person is. A human interpreter is then needed to conclude that the person is hiding, and probably (with certain probability) is still there. A Process Query System (PQS) is able to track and predict the path of arbitrary objects, based only on a description of their dynamic behavior, thus eliminating the need for precise identification of each object in every frame. The PQS is therefore able to draw human-like conclusions, allowing the system to track the person even when he/she is out of view. Additionally, using dynamic descriptions of tracked objects allows for low quality video signals, or even infra-red video, to be used for tracking. In this paper we introduce a novel way of implementing a video-based tracking system using a Process Query System to predict the position of objects in the environment, even after they have disappeared from view. Although the image processing pipeline is trivial, tracking accuracy is remarkably high, suggesting that overall performance can be improved even further with the use of more sophisticated video processing and image recognition technology.