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European Conference on Computer Vision

ECCV 2014: Computer Vision – ECCV 2014 pp 675–688Cite as

Recognizing Complex Events in Videos by Learning Key Static-Dynamic Evidences

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 8691)

Abstract

Complex events consist of various human interactions with different objects in diverse environments. The evidences needed to recognize events may occur in short time periods with variable lengths and can happen anywhere in a video. This fact prevents conventional machine learning algorithms from effectively recognizing the events. In this paper, we propose a novel method that can automatically identify the key evidences in videos for detecting complex events. Both static instances (objects) and dynamic instances (actions) are considered by sampling frames and temporal segments respectively. To compare the characteristic power of heterogeneous instances, we embed static and dynamic instances into a multiple instance learning framework via instance similarity measures, and cast the problem as an Evidence Selective Ranking (ESR) process. We impose ℓ1 norm to select key evidences while using the Infinite Push Loss Function to enforce positive videos to have higher detection scores than negative videos. The Alternating Direction Method of Multipliers (ADMM) algorithm is used to solve the optimization problem. Experiments on large-scale video datasets show that our method can improve the detection accuracy while providing the unique capability in discovering key evidences of each complex event.

Keywords

  • Video Event Detection
  • Infinite Push
  • Key Evidence Selection
  • ADMM

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Author information

Authors and Affiliations

  1. Graduate Institute of Electrical Engineering, National Taiwan University, Taiwan

    Kuan-Ting Lai & Ming-Syan Chen

  2. Research Center for IT Innovation, Academia Sinica, Taiwan

    Kuan-Ting Lai & Ming-Syan Chen

  3. Department of Electrical Engineering, Columbia University, USA

    Dong Liu & Shih-Fu Chang

Authors
  1. Kuan-Ting Lai
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  2. Dong Liu
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  3. Ming-Syan Chen
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  4. Shih-Fu Chang
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Toront, 6 King’s College Road, M5H 3S5, Toronto, ON, Canada

    David Fleet

  2. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague, Technicka 2, 166 27, Prague 6, Czech Republic

    Tomas Pajdla

  3. Max-Planck-Institut für Informatik, Campus E1 4, 66123, Saarbrücken, Germany

    Bernt Schiele

  4. ESAT - PSI, iMinds, KU Leuven, Kasteelpark Arenberg 10, Bus 2441, 3001, Leuven, Belgium

    Tinne Tuytelaars

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© 2014 Springer International Publishing Switzerland

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Lai, KT., Liu, D., Chen, MS., Chang, SF. (2014). Recognizing Complex Events in Videos by Learning Key Static-Dynamic Evidences. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8691. Springer, Cham. https://doi.org/10.1007/978-3-319-10578-9_44

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  • DOI: https://doi.org/10.1007/978-3-319-10578-9_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10577-2

  • Online ISBN: 978-3-319-10578-9

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