Dynamic object identification and verification using video

Baoxin Li, Rama Chellappa, Qinfen Zheng, Sandor Z. Der

Abstract: We introduce the concepts of dynamic object identification and verification using video. A generalized Hausdorff metric, which is more robust to noise and allows a confidence interpretation, is suggested for the identification and verification problem. Parameters from sensor motion compensation procedure are incorporated into the search step such that the Hausdorff metric based matching can be achieved efficiently under more complex transformation groups. An algorithm is proposed for identification/verification based on edge map matching using the generalized Hausdorff metric. Experiments on infrared video sequences are provided.