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Efficient Violence Detection Using Transfer Learning

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Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection (PAAMS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1472))

Abstract

In recent years several applications, namely in surveillance, human-computer interaction and video recovery based on its content has studied the detection and recognition of violence [22]. The purpose of violence detection is to automatically and effectively determine whether or not violence occurs in a short time. So, it is a crucial area since it will automatically enable the necessary means to stop the violence. To quickly solve this problem, we used models trained to solve general activity recognition problems such as Kinetics-400 to learn to extract general patterns that are very important to detect violent behaviour in videos. Our approach consists of using a state of the art pre-trained model in general activity recognition tasks (e.g. Kinetics-400) and then fine-tuning it to violence detection. We applied this approach in two violence datasets and achieved state-of-the-art results using only four input frames.

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Acknowledgment

This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n\(\circ \) 039334; Funding Reference: POCI-01–0247-FEDER- 039334].

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Authors and Affiliations

  1. Algorithm Center, University of Minho, Braga, Portugal

    Flávio Santos, Dalila Durães, Francisco S. Marcondes, José Machado & Paulo Novais

  2. Bosch Car Multimedia, Braga, Portugal

    Sascha Lange

Authors
  1. Flávio Santos
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  2. Dalila Durães
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  3. Francisco S. Marcondes
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  4. Sascha Lange
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  5. José Machado
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  6. Paulo Novais
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Corresponding author

Correspondence to Dalila Durães .

Editor information

Editors and Affiliations

  1. University of Salamanca, Salamanca, Spain

    Fernando De La Prieta

  2. German University in Cairo, New Cairo City, Egypt

    Alia El Bolock

  3. University of Minho, Braga, Portugal

    Dalila Durães

  4. Polytechnic Institute of Porto, ISEP, Porto, Portugal

    João Carneiro

  5. National Laboratory of Energy and Geology, Lisbon, Portugal

    Fernando Lopes

  6. Polytechnic University of Valencia, Valencia, Valencia, Spain

    Vicente Julian

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Santos, F., Durães, D., Marcondes, F.S., Lange, S., Machado, J., Novais, P. (2021). Efficient Violence Detection Using Transfer Learning. In: De La Prieta, F., El Bolock, A., Durães, D., Carneiro, J., Lopes, F., Julian, V. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection. PAAMS 2021. Communications in Computer and Information Science, vol 1472. Springer, Cham. https://doi.org/10.1007/978-3-030-85710-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-85710-3_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-85710-3

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