Tracking multiple moving objects by binary object forest segmentation

摘要

In an earlier study it was shown that the low level image segmentation technique known as binary object forest (BOF) analysis could be successfully used to extract one or two moving objects from complex backgrounds, even when the motion involved was very large. The method involved performing BOF analysis on each of a pair of images from a sequence and then matching the vertices of the resulting graphs. In the present study the problem of tracking multiple objects in complex backgrounds and in difficult circumstances such as partial occlusion, is considered. The approach taken is once again to perform an initial BOF analysis of each image but now to attempt matching over subgraphs of the BOF rather than simply on individual vertices. It is shown theoretically and experimentally that this results in a much more robust matching scheme. This increase in robustness not only allows multiple objects to be tracked but facilitates correct matching even when partial object occlusion occurs and when motion towards the sensor results in large (apparent) size changes between frames.