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Real-Time Detection and Tracking of Multiple Humans from High Bird’s-Eye Views in the Visual and Infrared Spectrum

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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Abstract

We propose a real-time system to detect and track multiple humans from high bird’s-eye views. First, we present a fast pipeline to detect humans observed from large distances by efficiently fusing information from a visual and infrared spectrum camera. The main contribution of our work is a new tracking approach. Its novelty lies in online learning of an objectness model which is used for updating a Kalman filter. We show that an adaptive objectness model outperforms a fixed model. Our system achieves a mean tracking loop time of 0.8 ms per human on a 2 GHz CPU which makes real time tracking of multiple humans possible.

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References

  1. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38, 13 (2006)

    Article  Google Scholar 

  2. Stalder, S., Grabner, H., Gool, L.: Dynamic objectness for adaptive tracking. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7726, pp. 43–56. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37431-9_4

    Chapter  Google Scholar 

  3. Liang, P., Pang, Y., Liao, C., Mei, X., Ling, H.: Adaptive objectness for object tracking, IEEE (2015)

    Google Scholar 

  4. Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 73–80. IEEE (2010)

    Google Scholar 

  5. Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.: BING: binarized normed gradients for objectness estimation at 300fps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3286–3293 (2014)

    Google Scholar 

  6. Yang, J., Yan, R., Hauptmann, A.G.: Adapting SVM classifiers to data with shifted distributions. In: Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), pp. 69–76. IEEE (2007)

    Google Scholar 

  7. Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)

    MathSciNet  MATH  Google Scholar 

  8. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45 (1960)

    Article  Google Scholar 

  9. Cuevas, E.V., Zaldivar, D., Rojas, R.: Kalman filter for vision tracking (2005)

    Google Scholar 

  10. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  11. Wu, Z., Fuller, N., Theriault, D., Betke, M.: A thermal infrared video benchmark for visual analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 201–208 (2014)

    Google Scholar 

  12. Davis, J.W., Keck, M.A.: A two-stage template approach to person detection in thermal imagery. WACV/MOTION 5, 364–369 (2005)

    Google Scholar 

  13. Davis, J.W., Sharma, V.: Background-subtraction using contour-based fusion of thermal and visible imagery. Comput. Vis. Image Underst. 106, 162–182 (2007)

    Article  Google Scholar 

  14. Vempati, A.S., Agamennoni, G., Stastny, T., Siegwart, R.: Victim detection from a fixed-wing UAV: experimental results. In: Bebis, G. (ed.) ISVC 2015. LNCS, vol. 9474, pp. 432–443. Springer, Heidelberg (2015). doi:10.1007/978-3-319-27857-5_39

    Chapter  Google Scholar 

  15. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47, 7–42 (2002)

    Article  MATH  Google Scholar 

  16. Vidas, S., Lakemond, R., Denman, S., Fookes, C., Sridharan, S., Wark, T.: A mask-based approach for the geometric calibration of thermal-infrared cameras. IEEE Trans. Instrum. Meas. 61, 1625–1635 (2012)

    Article  Google Scholar 

  17. Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34, 743–761 (2012)

    Article  Google Scholar 

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Acknowledgment

The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 600958 (SHERPA).

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Authors and Affiliations

  1. Autonomous Systems Lab, ETH Zurich, Zurich, Switzerland

    Julius Kümmerle, Timo Hinzmann, Anurag Sai Vempati & Roland Siegwart

Authors
  1. Julius Kümmerle
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  2. Timo Hinzmann
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  3. Anurag Sai Vempati
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  4. Roland Siegwart
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Corresponding author

Correspondence to Julius Kümmerle .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Kümmerle, J., Hinzmann, T., Vempati, A.S., Siegwart, R. (2016). Real-Time Detection and Tracking of Multiple Humans from High Bird’s-Eye Views in the Visual and Infrared Spectrum. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_49

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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