Advertisement

Multi Target Tracking Using Determinantal Point Processes

  • Felipe Jorquera
  • Sergio Hernández
  • Diego Vergara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

Multi Target Tracking has many applications such as video surveillance and event recognition among others. In this paper, we present a multi object tracking (MOT) method based on point processes and random finite sets theory. The Probability Hypothesis Density (PHD) filter is a MOT algorithm that deals with missed, false and redundant detections. However, the PHD filter, as well as other conventional tracking-by-detection approaches, requires some sort of pre-processing technique such as non-maximum suppression (NMS) to eliminate redundant detections. In this paper, we show that using NMS is sub-optimal and therefore propose Determinantal Point Processes (DPP) to select the final set of detections based on quality and similarity terms. We conclude that PHD filter-DPP method outperforms PHD filter-NMS.

Keywords

Multi object tracking Tracking-by-detection Determinantal Point Processes 

Notes

Acknowledgments

This work was supported by CONICYT/FONDECYT grant, project Robust Multi-Target Tracking using Discrete Visual Features, code 11140598.

References

  1. 1.
    Choi, W.: Near-online multi-target tracking with aggregated local flow descriptor. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3029–3037 (2015)Google Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  3. 3.
    Fagot-Bouquet, L., Audigier, R., Dhome, Y., Lerasle, F.: Improving multi-frame data association with sparse representations for robust near-online multi-object tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 774–790. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46484-8_47 CrossRefGoogle Scholar
  4. 4.
    Ferryman, J., Shahrokni, A.: Pets 2009: dataset and challenge. In: 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), pp. 1–6. IEEE (2009)Google Scholar
  5. 5.
    Kieritz, H., Becker, S., Hübner, W., Arens, M.: Online multi-person tracking using integral channel features. In: 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 122–130. IEEE (2016)Google Scholar
  6. 6.
    Kim, C., Li, F., Ciptadi, A., Rehg, J.M.: Multiple hypothesis tracking revisited. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4696–4704 (2015)Google Scholar
  7. 7.
    Leal-Taixé, L., Milan, A., Schindler, K., Cremers, D., Reid, I., Roth, S.: Tracking the trackers: an analysis of the state of the art in multiple object tracking. arXiv preprint arXiv:1704.02781 (2017)
  8. 8.
    Lee, D., Cha, G., Yang, M.-H., Oh, S.: Individualness and determinantal point processes for pedestrian detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 330–346. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46466-4_20 CrossRefGoogle Scholar
  9. 9.
    Mahler, R.P.: Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1152–1178 (2003)CrossRefGoogle Scholar
  10. 10.
    Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1201–1208. IEEE (2011)Google Scholar
  11. 11.
    Ristic, B., Clark, D., Vo, B.N., Vo, B.T.: Adaptive target birth intensity for PHD and CPHD filters. IEEE Trans. Aerosp. Electron. Syst. 48(2), 1656–1668 (2012)CrossRefGoogle Scholar
  12. 12.
    Ristic, B.: Particle Filters for Random Set Models. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-1-4614-6316-0 CrossRefMATHGoogle Scholar
  13. 13.
    Sadeghian, A., Alahi, A., Savarese, S.: Tracking the untrackable: learning to track multiple cues with long-term dependencies. arXiv preprint arXiv:1701.01909 (2017)
  14. 14.
    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
  15. 15.
    Tang, S., Andres, B., Andriluka, M., Schiele, B.: Multi-person tracking by multicut and deep matching. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 100–111. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48881-3_8 CrossRefGoogle Scholar
  16. 16.
    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

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Felipe Jorquera
    • 1
  • Sergio Hernández
    • 1
  • Diego Vergara
    • 1
  1. 1.Laboratorio GeoespacialUniversidad Católica del MauleTalcaChile

Personalised recommendations