A General Framework for Tracking Multiple People from a Moving Camera

TitleA General Framework for Tracking Multiple People from a Moving Camera
Publication TypeJournal Article
Year of Publication2012
AuthorsChoi, Wongun., Pantofaru, Caroline., and Savarese, Silvio
Refereed DesignationRefereed
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)
Date Published11/2012
Keywordsdetection, people, perception, tracking, vision
Abstract

 

In this paper, we present a general framework for tracking multiple, possibly interacting, people from a mobile vision platform. In order 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 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.

 

URLhttp://www-personal.umich.edu/~wgchoi/pami_track/
AttachmentSize
pami_preprint.pdf1.78 MB