Multi-target tracking via mixed integer optimization

Abstract

Given a set of target detections over several time periods, this paper addresses the multi-target tracking problem (MTT) of optimally assigning detections to targets and estimating the trajectory of the targets over time. MTT has been studied in the literature via predominantly probabilistic methods. In contrast to these approaches, we propose the use of mixed integer optimization (MIO) models and local search algorithms that are (a) scalable, as they provide near optimal solutions for six targets and ten time periods in milliseconds to seconds, (b) general, as they make no assumptions on the data, (c) robust, as they can accommodate missed and false detections of the targets, and (d) easily implementable, as they use at most two tuning parameters. We evaluate the performance of the new methods using a novel metric for complexity of an instance and find that they provide high quality solutions both reliably and quickly for a large range of scenarios, resulting in a promising approach to the area of MTT.