Multi-target Tracking Data Association Algorithm Based on Greedy Strategy
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TN953

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    Abstract:In this paper,a new association method is proposed to tackle the data association problem of multi-target tracking.In this algorithm, building the associative matrix with the Euclidean distance and the 1-Norm of state vector between tracks and points firstly.And using the associative matrix find the most suitable(Maximum matching success rate)points for every track. If the points just marked by one track, update this track directly; if the points marked by many tracks, choose the track with highest probability to update. Monte-Carlo Simulation experiments show that this algorithm guarantees the updating points for every tracks are the best points among all present points.

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Cite this article as: ZHANG Liang, WANG Yun-Feng. Multi-target Tracking Data Association Algorithm Based on Greedy Strategy [J]. J Sichuan Univ: Nat Sci Ed, 2018, 55: 0056.

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History
  • Received:March 21,2017
  • Revised:July 10,2017
  • Adopted:July 19,2017
  • Online: January 08,2018
  • Published: