Accurate detection of ellipses with false detection control at video rates using a gradient analysis

Accurate ellipse detection in image streams at real-time execution is an open challenge. We present a novel fast and robust ellipse detection method. The method adopts arcs selection, smart grouping, and repeated utilization of gradient information to significantly reduce the computations otherwise needed without compromising the detection effectiveness. Geometric properties calculable with few computations, such as arc smoothness, relative placement of curves, and region of confidence for ellipse centres, are utilized for this purpose. An exhaustive sensitivity analysis of the method's control parameters has been performed. It reveals range of values that support consistent performance over diverse challenging datasets with complex background, multiple differently sized ellipses, and occluded, overlapping ellipses. The method's performance is compared with six state-of-the-art detectors over four diverse datasets. Among all the tested methods, the proposed method demonstrates the best balance between detection effectiveness (the best or the second best F-measure scores) and computation time (>40 Hz) across all the datasets.