Increasing efficiency of Hausdorff approach for tracking real scenes with complex environments

Abstract

Tracking moving objects based on a Hausdorff approach can be formulated in terms of a matching process between two sets of edge points extracted from the object model to be localized and the corresponding frame of the image sequence. However, no information about the global measures of the object feature positions has been considered to carry out the matching process through a search strategy. This situation leads to an increase of computational cost in the tracking process, due to no limitations ("pruning") in the search region in the problem space. Experimental results with real-world complex image sequences including changing background conditions are provided to illustrate the performance and advantages with respect to previous approaches. The results are analyzed.