Multi-object tracking in video has the potential for broad applicability, from analyzing division-of-labor within biological networks such as insects, to surveillance and traffic monitoring. Despite any potential the field of multiple object tracking holds, it often remains passed over in favor of manual annotation methods due to difficulties in reliably obtaining accurate results. Existing tools for gathering tracking data are either very time consuming, inaccessible due to non-intuitive interfaces, or simply produce too many tracking failures with no means of correcting them. In this thesis, we address the multi-object tracking task that is ill served by existing tracking tools. Specifically, we discuss intuitive means of parameter tuning, training various subroutines of the tracking algorithm, and correcting tracking failures. To this end, we developed a new desktop application, ABCTracker, which provides a sequence of efficient steps the direct the user from beginning (the video) to the end (accurate tracking information).