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A General Framework for Tracking Multiple People from a Moving Camera
July 2013 (vol. 35 no. 7)
pp. 1577-1591
Wongun Choi, Dept. of Electr. & Comput. Eng., Univ. of Michigan, Ann Arbor, MI, USA
C. Pantofaru, Willow Garage, Inc., Menlo Park, CA, USA
S. Savarese, Dept. of Electr. & Comput. Eng., Univ. of Michigan, Ann Arbor, MI, USA
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.
Index Terms:
Cameras,Target tracking,Detectors,Face,Skin,Trajectory,RJ-MCMC particle filtering,Multitarget tracking,person detection,people tracking
Citation:
Wongun Choi, C. Pantofaru, S. Savarese, "A General Framework for Tracking Multiple People from a Moving Camera," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 7, pp. 1577-1591, July 2013, doi:10.1109/TPAMI.2012.248
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