Robust object tracking with fuzzy shape estimation

This work presents a novel, efficient and robust approach for object tracking based on sequences of video images. A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets with the set of image regions (blobs) extracted from each frame. Several numeric heuristics, describing the quality of gated groups of blobs and predicted tracks, are considered to generate confidence levels used in the update process. Rules are aimed to generate the most appropriate decisions under different conditions, emulating the reasoned decisions taken by an expert, and have been derived with a systematic analysis of performance. The application area is the Surveillance of airport surface, including situations with very closely spaced objects (aircraft and surface vehicles moving on apron). System performance with real image sequences of representative ground operations is shown at the end.