Over the past decades, a number of image analysis techniques have been developed in support of biomolecular studies. Majority of these techniques however are based on rudimentary principles. With the advent of computer vision techniques, more sophisticated tracking techniques in biological molecular imaging emerged. Currently, the most important of such techniques is light microscopy. However, achieving robustness and high accuracy in tracking and motion analysis in images obtained by light microscopy is hampered by three factors including the limited spatial resolution of the microscope, noise, and the large variability of biological image data. The latest generation of computational image analysis tools for (semi)-automated tracking of single molecules or molecular compounds within living cells promise to address these limitations. Although a number of these techniques are already available, the basic concepts uunderlying them are virtually the same. The common steps followed by these methods are as follows: preprocessing the image data, detecting individual particles per time point, linking particles detected at successive time points, and analyzing the results.