Identity Preserving Multi-People Tracking through Linear Programming

In this talk, I will show that, given probabilities of presence of people at various locations in individual time frames, finding the most likely set of trajectories amounts to solving a linear program that depends on very few parameters.
This can be done without requiring appearance information and in real-time, by using the K-Shortest Paths algorithm (KSP). However, this can result in unwarranted identity switches in complex scenes. In such cases, sparse image information can be used within the Linear Programming framework to keep track of people's identities, even when their paths come close to each other or intersect. By sparse, we mean that the appearance needs only be discriminative in a very limited number of frames, which makes our approach widely applicable.