Group Tracking: Exploring Mutual Relations for Multiple Object Tracking

Abstract

In this paper, we propose to track multiple previously unseen objects in unconstrained scenes. Instead of considering objects individually, we model objects in mutual context with each other to benefit robust and accurate tracking. We introduce a unified framework to combine both Individual Object Models (IOMs) and Mutual Relation Models (MRMs). The MRMs consist of three components, the relational graph to indicate related objects, the mutual relation vectors calculated within related objects to show the interactions, and the relational weights to balance all interactions and IOMs. As MRMs are varying along temporal sequences, we propose online algorithms to make MRMs adapt to current situations. We update relational graphs through analyzing object trajectories and cast the relational weight learning task as an online latent SVM problem. Extensive experiments on challenging real world video sequences demonstrate the efficiency and effectiveness of our framework.