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Learning with Adaptive Rate for Online Detection of Unusual Appearance

  • Conference paper
Advances in Visual Computing (ISVC 2014)

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

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Abstract

Detecting of unusual/abnormal event is a popular research in the area of event analysis. Unlike conventional methods that focus on the motion, we tackle a new problem for detecting an unusual appearance in a surveillance video. However, in case of appearance feature, static appearance is so dominant that the biased learning problem can occur. To avoid this problem, we propose a new learning scheme with adaptive learning rate. Moreover, to reduce the noisy detection, we also suggest a spatio-temporal decision scheme. Experimental results show the effectiveness of the proposed method to detect unusual appearances qualitatively and quantitatively.

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Author information

Authors and Affiliations

  1. Perception and Intelligence Laboratory Department of Electrical and Computer Engineering, ASRI Seoul National University, Seoul, South Korea

    Kimin Yun, Jiyun Kim, Soo Wan Kim, Hawook Jeong & Jin Young Choi

Authors
  1. Kimin Yun
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  2. Jiyun Kim
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  3. Soo Wan Kim
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  4. Hawook Jeong
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  5. Jin Young Choi
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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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Yun, K., Kim, J., Kim, S.W., Jeong, H., Choi, J.Y. (2014). Learning with Adaptive Rate for Online Detection of Unusual Appearance. 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_67

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_67

  • 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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