This paper proposes novel algorithms that use networkflow 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. 1.

I want to especially thank my advisor, Prof. Margrit Betke for her mentorship in every aspect of my research life. Margrit has been very patient to instruct me with every piece of my work in the past six years, providing extremely professional guidance and kind encouragement to help shape my research career. I would also like to acknowledge all