Omni-directional multiperson tracking in meeting scenarios combining annealing and particle filtering

This proposal deals with the topic of tracking an unknown number of persons with a monocular camera in indoor environments. Within this context the main tracking system requirements are defined not only by a robust determination of all human trajectories but also by a reliable recovery of all object identities especially for challenging situations like heavy occlusion or the reentry of a person. Regarding all these needs a novel approach has been developed combining a probabilistic particle filter framework with an heuristic simulated annealing technique ported to the tracking domain. While the inter frame correspondence of objects, i.e. the assignment of identities, is handled by the simulated annealing approach, the particle filter architecture will be responsible for both the classification of an object to be a person as well as a stable tracking of the respective trajectory. An active shape model is utilized to create weights for the particles and thus serves as an object classifier. Our system has been evaluated on several video sequences showing meeting scenarios with a different number of participants. Quantitative numbers based on a tracking evaluation scheme show, that our system is capable of not only accurately determining the number of persons visible in each scene but also of precisely tracking each human and correctly assigning a label.