Roadesic distance: Flow-aware tracklet association cost for wide area surveillance

Yeti Ziya Gurbuz, Ogul Can, A. Aydin Alatan

Abstract: Long-term multi-target tracking via tracklet merging in wide area surveillance has crucial importance to improve tracker performances and operational requirements. Min-cost network flow formulation for multi-target tracking is adopted for the tracklet merging problem. In order to improve the continuity of the computed flows by the min-cost network flow framework, a novel tracklet association cost is proposed to be utilized in this network. The proposed cost is based on connecting two tracklets by considering the traffic flow which is estimated from the precomputed tracklets. Such an approach enforces spatial consistencies between tracks by imposing these relations into the association cost. Hence, without violating the min-cost network flow formulation, a constraint to enforce spatial consistency can be implicitly obtained. The proposed cost function can be further exploited to interpolate gaps between the merged tracklets for postprocessing. The experimental results show that proposed association cost improves baseline framework that uses costs considering only two tracklets at a time, as well as some other tracklet merge algorithms from the literature.