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A deep unified framework for suspicious action recognition

  • Amine IlidrissiEmail author
  • Joo Kooi Tan
Original Article
  • 24 Downloads

Abstract

As action recognition undergoes change as a field under influence of the recent deep learning trend, and while research in areas such as background subtraction, object segmentation and action classification is steadily progressing, experiments devoted to evaluate a combination of the aforementioned fields, be it from a speed or a performance perspective, are far and few between. In this paper, we propose a deep, unified framework targeted towards suspicious action recognition that takes advantage of recent discoveries, fully leverages the power of convolutional neural networks and strikes a balance between speed and accuracy not accounted for in most research. We carry out performance evaluation on the KTH dataset and attain a 95.4% accuracy in 200 ms computational time, which compares favorably to other state-of-the-art methods. We also apply our framework to a video surveillance dataset and obtain 91.9% accuracy for suspicious actions in 205 ms computational time.

Keywords

Suspicious action recognition Deep learning Convolutional neural networks Background subtraction Optical flow estimation Action classification 

Notes

Acknowledgements

This research was supported by JSPS Kakenhi, Grant number 16K01554.

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Copyright information

© International Society of Artificial Life and Robotics (ISAROB) 2018

Authors and Affiliations

  1. 1.Kyushu Institute of TechnologyKitakyushuJapan

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