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Efficiency in Human Actions Recognition in Video Surveillance Using 3D CNN and DenseNet

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Advances in Information and Communication (FICC 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 438))

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Abstract

The human actions recognition in video is a topic of growing interest in the scientific community in computing, due to its application in real problems and different domains such as video surveillance, medicine, psychiatry, among others, and on the other hand, due to the overcrowding of video capture devices all over the planet. Processing video to extract characteristics and subsequent classification or recognition is a complex task, as it involves processing data in a spatial dimension (video dimensions) and a temporal dimension, causing the input data to increase abundantly and become in a challenging task. There are two approaches to the recognition of human actions on video; handcrafted approaches based on optical flow and approaches based on Deep Learning, the latter has achieved many achievements in terms of accuracy; however, it has the problem of high computational cost, making its application almost impossible in specific domains, much less in a real-time scenario. In this way, we propose an architecture based on Deep Learning, for human actions recognition in video, oriented to the domain of video surveillance and in a real-time scenario; For this, the proposal is based on an architecture that combines 3D CNN and DenseNet techniques. The results show that the proposal is efficient and can be used in the domain of real-time video surveillance. Likewise, general representations are proposed referring to the resolution and minimum frames per second that guarantee recognition.

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Author information

Authors and Affiliations

  1. José María Arguedas National University, Apurímac, Peru

    Herwin Alayn Huillcen Baca & Flor de Luz Palomino Valdivia

  2. San Agustin National University, Arequipa, Peru

    Juan Carlos Gutierrez Caceres

Authors
  1. Herwin Alayn Huillcen Baca
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  2. Juan Carlos Gutierrez Caceres
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  3. Flor de Luz Palomino Valdivia
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Corresponding author

Correspondence to Herwin Alayn Huillcen Baca .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

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Huillcen Baca, H.A., Gutierrez Caceres, J.C., de Luz Palomino Valdivia, F. (2022). Efficiency in Human Actions Recognition in Video Surveillance Using 3D CNN and DenseNet. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-030-98012-2_26

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