A novel square-root cubature informationweighted consensus filter algorithm for multi-target tracking in distributed camera networks

Yanming Chen*, Qingjie Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

This paper deals with the problem of multi-target tracking in a distributed camera network using the square-root cubature information filter (SCIF). SCIF is an efficient and robust nonlinear filter for multi-sensor data fusion. In camera networks, multiple cameras are arranged in a dispersed manner to cover a large area, and the target may appear in the blind area due to the limited field of view (FOV). Besides, each camera might receive noisy measurements. To overcome these problems, this paper proposes a novel multi-target square-root cubature information weighted consensus filter (MTSCF), which reduces the effect of clutter or spurious measurements using joint probabilistic data association (JPDA) and proper weights on the information matrix and information vector. The simulation results show that the proposed algorithm can efficiently track multiple targets in camera networks and is obviously better in terms of accuracy and stability than conventional multi-target tracking algorithms.

Original languageEnglish
Pages (from-to)10526-10546
Number of pages21
JournalSensors
Volume15
Issue number5
DOIs
Publication statusPublished - 4 May 2015

Keywords

  • Consensus algorithm
  • Cubature Kalman filter
  • Distributed camera networks
  • Information filter
  • Multi-target tracking

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