Automated tracking of colloidal clusters with sub-pixel accuracy and precision*This article belongs to the special issue: Emerging Leaders, which features invited work from the best early-career researchers working within the scope of Journal of Physics: Condensed Matter. This project is part of the Journal of Physics series' 50th anniversary celebrations in 2017. Daniela Kraft was selected by the Editorial Board of Journal of Physics: Condensed Matter as an Emerging Leader. (22nd November 2016) - van der Wel, Casper; Kraft, Daniela J
Abstract: Quantitative tracking of features from video images is a basic technique employed in many areas of science. Here, we present a method for the tracking of features that partially overlap, in order to be able to track so-called colloidal molecules. Our approach implements two improvements into existing particle tracking algorithms. Firstly, we use the history of previously identified feature locations to successfully find their positions in consecutive frames. Secondly, we present a framework for non-linear least-squares fitting to summed radial model functions and analyze the accuracy (bias) and precision (random error) of the method on artificial data. We find that our tracking algorithm correctly identifies overlapping features with an accuracy below 0.2% of the feature radius and a precision of 0.1 to 0.01 pixels for a typical image of a colloidal cluster. Finally, we use our method to extract the three-dimensional diffusion tensor from the Brownian motion of colloidal dimers.