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Detection of Toy Soldiers Taken from a Bird’s Perspective Using Convolutional Neural Networks

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ICT Innovations 2019. Big Data Processing and Mining (ICT Innovations 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1110))

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Abstract

This paper describes the use of two different deep-learning approaches for object detection to recognize a toy soldier. We use recordings of toy soldiers in different poses under different scenarios to simulate appearance of persons on footage taken by drones. Recordings from a bird’s eye view are today widely used in the search for missing persons in non-urban areas, border control, animal movement control, and the like. We have compared the single-shot multi-box detector (SSD) with the MobileNet or Inception V2 as a backbone, SSDLite with MobileNet and Faster R-CNN combined with Inception V2 and ResNet50. The results show that Faster R-CNN detects small object such as toy soldiers more successfully than SSD, and the training time of Faster R-CNN is much shorter than that of SSD.

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Acknowledgment

This research was supported by Croatian Science Foundation under the project IP-2016-06-8345 “Automatic recognition of actions and activities in multimedia content from the sports domain” (RAASS) and by the University of Rijeka under the project number 18-222-1385.

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Authors and Affiliations

  1. Department of Informatics, University of Rijeka, Rijeka, Croatia

    Saša Sambolek & Marina Ivašić-Kos

Authors
  1. Saša Sambolek
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  2. Marina Ivašić-Kos
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Corresponding author

Correspondence to Marina Ivašić-Kos .

Editor information

Editors and Affiliations

  1. Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia

    Sonja Gievska

  2. Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia

    Gjorgji Madjarov

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Sambolek, S., Ivašić-Kos, M. (2019). Detection of Toy Soldiers Taken from a Bird’s Perspective Using Convolutional Neural Networks. In: Gievska, S., Madjarov, G. (eds) ICT Innovations 2019. Big Data Processing and Mining. ICT Innovations 2019. Communications in Computer and Information Science, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-030-33110-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-33110-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33109-2

  • Online ISBN: 978-3-030-33110-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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