Human-centered Multi-target Tracking: A Random Set Theoretic Approach

The incorporation of soft human-generated data into the fusion process is an emerging trend in the data fusion community. This paper describes an extension of our original Random Set (RS) theoretic soft/hard data fusion system from single-target to multi-target tracking case. Leveraging recent developments in the RS theoretic data fusion community, we propose a novel soft measurement-to-track association algorithm. Based on this algorithm, we describe a multi-target tracking system capable of processing soft human-generated data. Our preliminary experiments demonstrate the advantages of the proposed soft data association algorithm (SDAA) in achieving substantial improvement of tracking performance, considering the baseline algorithm to rely merely on human opinions for solving the data association problem.