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Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking

  • Loïc Fagot-BouquetEmail author
  • Romaric Audigier
  • Yoann Dhome
  • Frédéric Lerasle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9912)

Abstract

Multiple Object Tracking still remains a difficult problem due to appearance variations and occlusions of the targets or detection failures. Using sophisticated appearance models or performing data association over multiple frames are two common approaches that lead to gain in performances. Inspired by the success of sparse representations in Single Object Tracking, we propose to formulate the multi-frame data association step as an energy minimization problem, designing an energy that efficiently exploits sparse representations of all detections. Furthermore, we propose to use a structured sparsity-inducing norm to compute representations more suited to the tracking context. We perform extensive experiments to demonstrate the effectiveness of the proposed formulation, and evaluate our approach on two public authoritative benchmarks in order to compare it with several state-of-the-art methods.

Keywords

Multiple Object Tracking Tracking by detection Multiple frame data association Sparse representation MCMC sampling 

Supplementary material

Supplementary material 1 (avi 16472 KB)

Supplementary material 2 (avi 6925 KB)

419983_1_En_47_MOESM3_ESM.pdf (92 kb)
Supplementary material 3 (pdf 91 KB)
419983_1_En_47_MOESM4_ESM.pdf (166 kb)
Supplementary material 4 (pdf 166 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Loïc Fagot-Bouquet
    • 1
    Email author
  • Romaric Audigier
    • 1
  • Yoann Dhome
    • 1
  • Frédéric Lerasle
    • 2
    • 3
  1. 1.CEA, LIST, Vision and Content Engineering LaboratoryGif-sur-YvetteFrance
  2. 2.CNRS, LAASToulouseFrance
  3. 3.Université de Toulouse, UPS, LAASToulouseFrance

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