title: A general framework for tracking multiple people from a moving camera.
abstract: In this paper, we present a general framework for tracking multiple, possibly interacting, people from a mobile vision platform. To determine all of the trajectories robustly and in a 3D coordinate system, we estimate both the camera's ego-motion and the people's paths within a single coherent framework. The tracking problem is framed as finding the MAP solution of a posterior probability, and is solved using the reversible jump Markov chain Monte Carlo (RJ-MCMC) particle filtering method. We evaluate our system on challenging datasets taken from moving cameras, including an outdoor street scene video dataset, as well as an indoor RGB-D dataset collected in an office. Experimental evidence shows that the proposed method can robustly estimate a camera's motion from dynamic scenes and stably track people who are moving independently or interacting.
authors.affiliation: Department of Electrical and Computer Engineering, University of Michigan, Room 4435, 1301 Beal Avenue, Ann Arbor, MI 48109-2122, USA. wgchoi@umich.edu
authors.forename: Wongun
authors.initials: W
authors.lastname: Choi
authors.forename: Caroline
authors.initials: C
authors.lastname: Pantofaru
authors.forename: Silvio
authors.initials: S
authors.lastname: Savarese
mesh_terms: D006802:Human Activities / Q000145:classification*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D009010:Monte Carlo Method; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014741:Video Recording / Q000379:methods*