Tracking People by Evolving Social Groups: An Approach with Social Network Perspective

Abstract

We address the problem of multi-people tracking in unconstrained and semi-crowded scenes. People typically walk in groups that split and merge over time. The evolving or dynamic social group property embodies pedestrians' connections and interactions during walking which we attempt to identify and exploit in this paper. To this end, instead of seeking more robust appearance or motion models to track each person as an isolated moving entity, we pose the multi-people tracking problem as a group-based tracklets association problem using the discovered social groups of track lets as the contextual cues. We formulate tracking the evolution of social groups of tracklets as detecting closely connected communities in a "tracklet interaction network" (TIN) with nodes standing for the tracklets and edges denoting the spatio-temporal co-occurrence correlations measured by the edge weights. We incorporate the detected social groups in the tracklet interaction network to improve multi-people tracking performance. We evaluate our approach against state-of-the-art and show improvements on three real-world datasets.