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Pickpocketing Recognition in Still Images

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12664))

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Abstract

Human activity recognition (HAR) is a challenging topic in the computer vision field. Pickpocketing is a type of human criminal actions. It needs extensive research and development for detection. This paper researches how it’s possible of pickpocketing recognition in still images. This paper takes consideration both of classification and detection. We develop our models from state-of-art pre-trained models: VGG16, ResNet50, ResNet101, and ResNet152. Moreover, we also include a convolutional block attention module (CBAM [27]) in the model. The attention mechanism enhances model performances by focusing on informative features. For classification, the highest accuracy (89%) is ResNet152 with CBAM [27] (ResNet152+CBAM). We also examine pickpocketing detection on RetinaNet [14] and YOLOv.3 [34]. The mean average precision (mAP) of pickpocketing detection is consistent with Redmon et al. [34]. RetinaNet’s precision (80 mAP) is higher than YOLOv.3 (78 mAP), but YOLOv.3 is much faster detection. ResNet152+CBAM model detection on RetinaNet approach provides the highest mAP. However, it is much slower detection than YOLOv.3 (only 10 ms). This paper proves that It is possible to implement pickpocketing on still images in a reliable time and with outstanding accuracy. This proposed model possibly apply to the other HAR tasks.

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Authors and Affiliations

  1. Institute of Artificial Intelligence, De Montfort University, Leicester, LE1 9BH, UK

    Prisa Damrongsiri & Hossein Malekmohamadi

Authors
  1. Prisa Damrongsiri
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  2. Hossein Malekmohamadi
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Corresponding author

Correspondence to Prisa Damrongsiri .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell'Informazione, University of Firenze, Florence, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Damrongsiri, P., Malekmohamadi, H. (2021). Pickpocketing Recognition in Still Images. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-68799-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68798-4

  • Online ISBN: 978-3-030-68799-1

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