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Visual Tracking Extensions for Accurate Target Recovery in Low Frame Rate Videos

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

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Abstract

Visual tracking in low frame rate videos has many inherent difficulties for achieving accurate target recovery, such as occlusions, abrupt motions and rapid pose changes. Thus, conventional tracking methods cannot be applied reliably. In this paper, we offer a new scheme for tracking objects in low frame rate videos. We present a method of integrating multiple metrics for template matching, as an extension for the particle filter. By inspecting a large data set of videos for tracking, we show that our method not only outperforms other related benchmarks in the field, but it also achieves better results both visually and quantitatively, once compared to actual ground truth data.

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References

  1. Zhong, B., Yao, H., Chen, S., Ji, R., Chin, T.J., Wang, H.: Visual tracking via weakly supervised learning from multiple imperfect oracles. Pattern Recognition 47, 1395–1410 (2014)

    Article  Google Scholar 

  2. Oron, S., Bar-Hillel, A., Levi, D., Avidan, S.: Locally orderless tracking. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1940–1947. IEEE (2012)

    Google Scholar 

  3. Porikli, F., Tuzel, O.: Object tracking in low-frame-rate video. In: International Society for Optics and Photonics Electronic Imaging 2005, pp. 72–79 (2005)

    Google Scholar 

  4. Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998)

    Article  Google Scholar 

  5. Tomasi, C., Kanade, T.: Detection and tracking of point features. School of Computer Science, Carnegie Mellon Univ. Pittsburgh (1991)

    Google Scholar 

  6. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 142–149. IEEE (2000)

    Google Scholar 

  7. Ling, H., Wu, Y., Blasch, E., Chen, G., Lang, H., Bai, L.: Evaluation of visual tracking in extremely low frame rate wide area motion imagery. In: 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2011)

    Google Scholar 

  8. Palaniappan, K., Bunyak, F., Kumar, P., Ersoy, I., Jaeger, S., Ganguli, K., Haridas, A., Fraser, J., Rao, R.M., Seetharaman, G.: Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video. In: 2010 13th Conference on Information fusion (FUSION), pp. 1–8. IEEE (2010)

    Google Scholar 

  9. Li, Y., Ai, H., Yamashita, T., Lao, S., Kawade, M.: Tracking in low frame rate video: A cascade particle filter with discriminative observers of different life spans. IEEE Tran. on Pattern Analysis and Machine Intelligence 30, 1728–1740 (2008)

    Article  Google Scholar 

  10. Liu, C., Shum, H.-Y., Zhang, C.: Hierarchical shape modeling for automatic face localization. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 687–703. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  11. Okuma, K., Taleghani, A., de Freitas, N., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Chang, C., Ansari, R.: Kernel particle filter for visual tracking. IEEE Signal Processing Letters 12, 242–245 (2005)

    Article  Google Scholar 

  13. Jiang, Y., Li, Z., Fang, J., Yue, J., Li, D.: Automatic video tracking of chinese mitten crabs based on the particle filter algorithm using a biologically constrained probe and resampling. Computers and Electronics in Agriculture 106

    Google Scholar 

  14. Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: 6th International Conference on Computer Vision, pp. 59–66. IEEE (1998)

    Google Scholar 

  15. Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  16. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22, 61–79 (1997)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Elbit Systems Ltd., Haifa, Israel

    Yoav Liberman & Adi Perry

Authors
  1. Yoav Liberman
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  2. Adi Perry
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

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

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Santa Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Liberman, Y., Perry, A. (2014). Visual Tracking Extensions for Accurate Target Recovery in Low Frame Rate Videos. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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