Deep Recurrent Neural Network for Multi-target Filtering

  • Mehryar EmambakhshEmail author
  • Alessandro Bay
  • Eduard Vazquez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)


This paper addresses the problem of fixed motion and measurement models for multi-target filtering using an adaptive learning framework. This is performed by defining target tuples with random finite set terminology and utilisation of recurrent neural networks with a long short-term memory architecture. A novel data association algorithm compatible with the predicted tracklet tuples is proposed, enabling the update of occluded targets, in addition to assigning birth, survival and death of targets. The algorithm is evaluated over a commonly used filtering simulation scenario, with highly promising results (


Multi-target filtering Recurrent neural network Random finite sets Long short-term memory 


  1. 1.
    Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: OSDI, pp. 265–283 (2016)Google Scholar
  2. 2.
    Bay, A., Lepsoy, S., Magli, E.: Stable limit cycles in recurrent neural networks. In: 2016 International Conference on Communications (COMM), pp. 89–92 (2016)Google Scholar
  3. 3.
    Emambakhsh, M., Evans, A.: Nasal patches and curves for expression-robust 3D face recognition. IEEE Trans. PAMI 39(5), 995–1007 (2017)CrossRefGoogle Scholar
  4. 4.
    Fantacci, C., Vo, B.N., Vo, B.T., Battistelli, G., Chisci, L.: Robust fusion for multisensor multiobject tracking. IEEE Signal Process. Lett. 25(5), 640–644 (2018)CrossRefGoogle Scholar
  5. 5.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  6. 6.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  7. 7.
    Mahler, R.: PHD filters of higher order in target number. IEEE Trans. Aerosp. Electron. Syst. 43(4), 1523–1543 (2007)CrossRefGoogle Scholar
  8. 8.
    Mahler, R.P.S.: Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1152–1178 (2003)CrossRefGoogle Scholar
  9. 9.
    Milan, A., Rezatofighi, S., Dick, A., Reid, I., Schindler, K.: Online multi-target tracking using recurrent neural networks. In: AAAI Conference on Artificial Intelligence Thirty (2017)Google Scholar
  10. 10.
    Nagappa, S., Delande, E.D., Clark, D.E., Houssineau, J.: A tractable forward-backward CPHD smoother. IEEE Trans. Aerosp. Electron. Syst. 53(1), 201–217 (2017)CrossRefGoogle Scholar
  11. 11.
    Reuter, S., Vo, B.T., Vo, B.N., Dietmayer, K.: The labeled multi-Bernoulli filter. IEEE Trans. Signal Process. 62(12), 3246–3260 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Schuhmacher, D., Vo, B.T., Vo, B.N.: A consistent metric for performance evaluation of multi-object filters. IEEE Trans. Signal Process. 56(8), 3447–3457 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Vo, B.N., Ma, W.K.: The Gaussian mixture probability hypothesis density filter. IEEE Trans. Signal Process. 54(11), 4091–4104 (2006)CrossRefGoogle Scholar
  14. 14.
    Vo, B.N., Singh, S., Doucet, A.: Sequential Monte Carlo methods for multitarget filtering with random finite sets. IEEE Trans. Aerosp. Electron. Syst. 41(4), 1224–1245 (2005)CrossRefGoogle Scholar
  15. 15.
    Vo, B.N., Vo, B.T., Hoang, H.G.: An efficient implementation of the generalized labeled multi-Bernoulli filter. IEEE Trans. Signal Process. 65(8), 1975–1987 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Vo, B.N., Vo, B.T., Phung, D.: Labeled random finite sets and the Bayes multi-target tracking filter. IEEE Trans. Signal Process. 62(24), 6554–6567 (2014)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Vo, B.N., Singh, S., Doucet, A.: Sequential Monte Carlo implementation of the PHD filter for multi-target tracking. In: Proceedings of the Sixth International Conference of Information Fusion, vol. 2, pp. 792–799 (2003)Google Scholar
  18. 18.
    Vorontsov, E., Trabelsi, C., Kadoury, S., Pal, C.: On orthogonality and learning recurrent networks with long term dependencies. arXiv preprint arXiv:1702.00071 (2017)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mehryar Emambakhsh
    • 1
    Email author
  • Alessandro Bay
    • 1
  • Eduard Vazquez
    • 1
  1. 1.Cortexica Vision SystemsLondonUK

Personalised recommendations