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Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

Abstract

Data storage has been a problem as technology advances, there are more devices capable of capturing images, sounds, videos, etc. On the security side, many people choose to use security cameras that are available 24 h a day to capture anomalous events and maintain the security of the area, however, storing all captured videos generates high costs, as well as the prolonged analysis that this type of videos implies. For this reason, we propose a method that allows selecting only the important events captured by a video surveillance camera and then classifying them among the types of most constant criminal acts in Peru.

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Notes

  1. 1.

    Seagate - https://bit.ly/31wPnfn.

  2. 2.

    Magazine “Lima como Vamos” - https://bit.ly/394n6i3.

  3. 3.

    INEI - https://www.inei.gob.pe/.

  4. 4.

    https://www.crcv.ucf.edu/projects/real-world/.

  5. 5.

    “Lima como Vamos” - https://bit.ly/2BAdj78.

  6. 6.

    INEI - https://bit.ly/39F7FgD.

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Correspondence to Willy Ugarte .

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Chancolla-Neira, S.W., Salinas-Lozano, C.E., Ugarte, W. (2021). Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_20

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  • DOI: https://doi.org/10.1007/978-3-030-76228-5_20

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