This paper proposes a number of methods to evaluate features in the context of people tracking in complex environments. Such environments will have varying lighting conditions (the subject of this paper), occlusions by other people and objects, as well as a varying number of people. The paper concentrates on edge features because of their insensitivity to changes in illumination and camera movements. It assumes that some form of model-based processing will be used for recognition and tracking so as to be able to deal with partially visible people. This requires the adaptive choice of what parts of people need to be tracked using the best combination of features. A number of measures are proposed to quantify edge performance that are illustrated for a number of edge detectors on a number of video sequences that have different properties or contexts.
['Gladis John', 'Geoff A. W. West', 'Mihai Lazarescu']
Measures for the evaluation of segmentation methods used in model based people tracking methods