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Action Recognition Based on Divide-and-Conquer

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Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11729))

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Abstract

Recently, deep convolutional neural networks have made great breakthroughs in the field of action recognition. Since sequential video frames have a lot of redundant information, compared with dense sampling, sparse sampling network can also achieve good results. Due to sparse sampling’s limitation of access to information, this paper mainly discusses how to further improve the learning ability of the model based on sparse sampling. We proposed a model based on divide-and-conquer, which use a threshold α to determine whether action data require sparse sampling or dense local sampling for learning. Finally, our approach obtains the state-the-of-art performance on the datasets of HMDB51 (72.4%) and UCF101 (95.3%).

This work is supported by the National Key R&D Program of China (2018YFB0203904), National Natural Science Foundation of China (61602165), Natural Science Foundation of Hunan Province (2018JJ3074), NSFC from PRC (61872137, 61502158), Hunan NSF (2017JJ3042).

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Authors and Affiliations

  1. Hunan University, Changsha, 10532, China

    Guanghua Tan, Rui Miao & Yi Xiao

Authors
  1. Guanghua Tan
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  2. Rui Miao
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  3. Yi Xiao
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Corresponding author

Correspondence to Guanghua Tan .

Editor information

Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Praha 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Tan, G., Miao, R., Xiao, Y. (2019). Action Recognition Based on Divide-and-Conquer. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-30508-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30507-9

  • Online ISBN: 978-3-030-30508-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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