Filling in the blanks: reconstructing microscopic crowd motion from multiple disparate noisy sensors

Tracking the movement of individuals in a crowd is an indispensable component to reconstructing crowd movement, with applications in crowd surveillance and data-driven animation. Typically, multiple sensors are distributed over wide area and often they have incomplete coverage of the area or the input introduces noise due to the tracking algorithm or hardware failure. In this paper, we propose a novel refinement method that complements existing crowd tracking solutions to reconstruct a holistic view of the microscopic movement of individuals in a crowd, from noisy tracked data with missing and even incomplete information. Central to our approach is a global optimization based trajectory estimation with modular objective functions. We empirically demonstrate the potential utility of our approach in various scenarios that are standard in crowd dynamic analysis and simulations.