Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques

To design a robust swimmer tracking system, we took into account two well-known tracking techniques: the nonlinear joint transform correlation (NL-JTC) and the color histogram. The two techniques perform comparably well, yet they both have substantial limitations. Interestingly, they also seem to show some complementarity. The correlation technique yields accurate detection but is sensitive to rotation, scale and contour deformation, whereas the color histogram technique is robust for rotation and contour deformation but shows low accuracy and is highly sensitive to luminosity and confusing background colors. These observations suggested the possibility of a dynamic fusion of the correlation plane and the color scores map. Before this fusion, two steps are required. First is the extraction of a sub-plane of correlation that describes the similarity between the reference and target images. This sub-plane has the same size as the color scores map but they have different interval values. Thus, the second step is required which is the normalization of the planes in the same interval so they can be fused. In order to determine the benefits of this fusion technique, first, we tested it on a synthetic image containing different forms with different colors. We thus were able to optimize the correlation plane and color histogram techniques before applying our fusion technique to real videos of swimmers in international competitions. Last, a comparative study of the dynamic fusion technique and the two classical techniques was carried out to demonstrate the efficacy of the proposed technique. The criteria of comparison were the tracking percentage, the peak to correlation energy (PCE), which evaluated the sharpness of the peak (accuracy), and the local standard deviation (Local-STD), which assessed the noise in the planes (robustness).