Leveraging long-term predictions and online learning in agent-based multiple person tracking

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

30 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)399-410
Journal / PublicationIEEE Transactions on Circuits and Systems for Video Technology
Volume25
Issue number3
Online published29 Jul 2014
Publication statusPublished - Mar 2015

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Abstract

We present a multiple-person tracking algorithm, based on combining particle filters (PFs) and reciprocal velocity obstacle (RVO), an agent-based crowd model that infers collision-free velocities so as to predict a pedestrian's motion. In addition to position and velocity, our tracking algorithm can estimate the internal goals (desired destination or desired velocity) of the tracked pedestrian in an online manner, thus removing the need to specify this information beforehand. Furthermore, we leverage the longer term predictions of RVO by deriving a higher order PF, which aggregates multiple predictions from different prior time steps. This yields a tracker that can recover from short-term occlusions and spurious noise in the appearance model. Experimental results show that our tracking algorithm is suitable for predicting pedestrians' behaviors online without needing scene priors or hand-annotated goal information, and improves tracking in real-world crowded scenes under low frame rates.

Research Area(s)

  • pedestrian motion model, Pedestrian tracking, video surveillance, Particle filter (PF)

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