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Monocular and Binocular People Tracking

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Computer Vision
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Synonyms

Person following

Related Concepts

Definition

People tracking is the process of estimating and recording the locations of target people in 2D image sequences (monocular computer vision) or 3D spaces over time (binocular computer vision). It may also refer to the process of estimating and recording the pose (or joint locations) of target people.

Background

Visual object tracking is a fundamental problem in computer vision. As a person is the most important type of object, people tracking has received tremendous interest from both academia and industry. While generic object tracking methods can be directly applied to people tracking, there are also many algorithms and schemes that are tailored for people tracking.

Monocular People Tracking

Monocular people tracking finds applications in surveillance, video indexing, and many other video analytic applications. When a specific person is considered, people tracking can be treated as a generic visual object...

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References

  1. Shi J, Tomasi C (1993) Good features to track. Technical report, Cornell University

    Google Scholar 

  2. Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 2544–2550. IEEE

    Google Scholar 

  3. Henriques J, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Article  Google Scholar 

  4. Danelljan M, Häger G, Khan FS, Felsberg M (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575

    Article  Google Scholar 

  5. Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS (2016) Fully-convolutional siamese networks for object tracking. In: European conference on computer vision, pp 850–865

    Google Scholar 

  6. Li B, Yan J, Wu W, Zhu Z, Hu X (2018) High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8971–8980

    Google Scholar 

  7. Xiang Y, Alahi A, Savarese S (2015) Learning to track: Online multi-object tracking by decision making. In: Proceedings of the IEEE international conference on computer vision, pp 4705–4713

    Google Scholar 

  8. Berclaz J, Fleuret F, Turetken E, Fua P (2011) Multiple object tracking using k-shortest paths optimization. IEEE Trans Pattern Anal Mach Intell 33(9):1806–1819

    Article  Google Scholar 

  9. Leibe B, Schindler K, Van Gool L (2007) Coupled detection and trajectory estimation for multi-object tracking. In: 2007 IEEE 11th international conference on computer vision, pp 1–8. IEEE

    Google Scholar 

  10. Andriluka M, Roth S, Schiele B (2008) People-tracking-by-detection and people-detection-by-tracking. In: 2008 IEEE conference on computer vision and pattern recognition, pp 1–8. IEEE

    Google Scholar 

  11. Breitenstein MD, Reichlin F, Leibe B, Koller-Meier E, Van Gool L (2009) Robust tracking-by-detection using a detector confidence particle filter. In: 2009 IEEE 12th international conference on computer vision, pp 1515–1522. IEEE

    Google Scholar 

  12. Tang S, Andriluka M, Milan A, Schindler K, Roth S, Schiele B (2013) Learning people detectors for tracking in crowded scenes. In: Proceedings of the IEEE international conference on computer vision, pp 1049–1056

    Google Scholar 

  13. Andriluka M, Roth S, Schiele B (2010) Monocular 3D pose estimation and tracking by detection. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 623–630. IEEE

    Google Scholar 

  14. Shu G, Dehghan A, Oreifej O, Hand E, Shah M (2012) Part-based multiple-person tracking with partial occlusion handling. In: 2012 IEEE conference on computer vision and pattern recognition, pp 1815–1821. IEEE

    Google Scholar 

  15. Henschel R, Leal-Taixé L, Cremers D, Rosenhahn B (2018) Fusion of head and full-body detectors for multi-object tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1428–1437

    Google Scholar 

  16. Tang S, Andriluka M, Andres B, Schiele B (2017) Multiple people tracking by lifted multicut and person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3539–3548

    Google Scholar 

  17. Chen Z, Birchfield ST (2007) Person following with a mobile robot using binocular feature-based tracking. In: 2007 IEEE/RSJ international conference on intelligent robots and systems, pp 815–820. IEEE

    Google Scholar 

  18. Ess A, Leibe B, Schindler K, Van Gool L (2009) Robust multiperson tracking from a mobile platform. IEEE Trans Pattern Anal Mach Intell 31(10):1831–1846

    Article  Google Scholar 

  19. Luber M, Spinello L, Arras KO (2011) People tracking in rgb-d data with on-line boosted target models. In: 2011 IEEE/RSJ international conference on intelligent robots and systems, pp 3844–3849. IEEE

    Google Scholar 

  20. Cao L, Wang C, Li J (2015) Robust depth-based object tracking from a moving binocular camera. Signal Process 112:154–161

    Article  Google Scholar 

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Correspondence to Wenjun Zeng .

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Luo, C., Zeng, W. (2021). Monocular and Binocular People Tracking. In: Ikeuchi, K. (eds) Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-63416-2_872

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