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Neuro-probabilistic model for object tracking

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Abstract

Occlusion is one of the major challenges for object tracking in real-life scenario. Various techniques in particle filter framework have been developed to solve this problem. This framework depends on two issues, namely motion model and observation (i.e., likelihood) model. Due to the lack of effective observation model and efficient motion model, problem of occlusion still remains unsolvable in the tracking task. In this article, an effective observation model is proposed based on confidence (classification) score provided by the developing online probabilistic neural network-based discriminative appearance model. The appearance model is trained with the prior knowledge of two classes (i.e., object and background) and tries to discriminate between three classes, namely object, background and occluded part of the object. The considered composite motion model can handle both the object motion and scale change in the object. The proposed update mechanism is able to adapt the appearance change in an object during tracking. We show a realization of the proposed method and demonstrate its performance (both quantitatively and qualitatively) with respect to state-of-the-art techniques on several challenging sequences. Analysis of the results concludes that the proposed technique can track fully (or partially) occluded object as well as object in various complex environments in a better way as compared to the existing ones.

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Notes

  1. In this article, target candidate, test, tth and current frame are used interchangeably.

  2. http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html.

  3. http://crcv.ucf.edu/data/ALOV++/.

  4. http://www.reefvid.org/.

  5. http://www.votchallenge.net/vot2015/dataset.html.

  6. http://cs-people.bu.edu/jmzhang/MEEM/MEEM.html.

  7. http://www.cvl.isy.liu.se/en/research/objrec/visualtracking/scalvistrack/index.html.

  8. http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html.

  9. http://www.dabi.temple.edu/~hbling/code/TGPR.htm.

  10. http://winsty.net/dlt.html.

  11. http://faculty.ucmerced.edu/mhyang/project/cvpr12_jia_project.htm.

  12. http://faculty.ucmerced.edu/mhyang/project/cvpr12_scm.htm.

  13. http://sites.google.com/site/zhangtianzhu2012/publications.

  14. http://github.com/zk00006/OpenTLD.

  15. http://www.cs.cityu.edu.hk/~shengfehe2/visual-tracking-via-locality-sensitive-histograms.html.

  16. http://www.umiacs.umd.edu/~fyang/spt.html.

  17. http://home.isr.uc.pt/~henriques/circulant/.

  18. http://www.eng.tau.ac.il/~oron/LOT/LOT.html.

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Acknowledgements

The author would like to thank Prof. CV Jawahar, CVIT, International Institute of Information Technology, Hyderabad, India, for his support during revision of this manuscript.

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    Ajoy Mondal

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Mondal, A. Neuro-probabilistic model for object tracking. Pattern Anal Applic 22, 1609–1628 (2019). https://doi.org/10.1007/s10044-019-00791-6

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