Efficient track linking methods for track graphs using network-flow and set-cover techniques

This paper proposes novel algorithms that use network-flow and set-cover techniques to perform occlusion reasoning for a large number of small, moving objects in single or multiple views. We designed a track-linking framework for reasoning about short-term and long-term occlusions. We introduce a two-stage network-flow process to automatically construct a “track graph” that describes the track merging and splitting events caused by occlusion. To explain short-term occlusions, when local information is sufficient to distinguish objects, the process links trajectory segments through a series of optimal bipartite-graph matches. To resolve long-term occlusions, when global information is needed to characterize objects, the linking process computes a logarithmic approximation solution to the set cover problem. If multiple views are available, our method builds a track graph, independently for each view, and then simultaneously links track segments from each graph, solving a joint set cover problem for which a logarithmic approximation also exists. Through experiments on different datasets, we show that our proposed linear and integer optimization techniques make the track graph a particularly useful tool for tracking large groups of individuals in images.