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Spatio-Temporal Action Localization for Pedestrian Action Detection

  • Linchao He
  • Jiong MuEmail author
  • Mengting Luo
  • Yunlu Lu
  • Xuefeng Tan
  • Dejun Zhang
Conference paper
  • 8 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 551)

Abstract

Current state-of-the-art temporal action detection methods are focused on untrimmed, multi-target videos. As popularized in the object detection framework, these methods perform classification on action class and detection of the duration for multiple instances. But these methods are unrealistic because the action of targets is usually irrelevant and complex in real-world. The previous methods utilize optical flow to handle multiple instances, but they cost too much time on estimating optical flow for evaluating. Inspired by spatio-temporal action detection, we improve the previous method with a new pedestrian action detection network which can detect a pedestrian in real-time. We replace Single Shot Multi-Box Detection (SSD) with RFB-Net which is more efficiency. The tube linking algorithm is introduced to link bounding boxes to different action instances. We use pedestrian action detection network to only process RGB frames which cost less time compared to two-stream based methods. Our framework achieves comparable result compared to the state-of-the-art and can detect in real-time.

Keywords

Neural network Object detection Action recognition 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant 61702350, and the Sichuan Provence Department of Education (NO. 17ZA0297).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Linchao He
    • 1
    • 2
  • Jiong Mu
    • 1
    • 2
    Email author
  • Mengting Luo
    • 1
    • 2
  • Yunlu Lu
    • 1
  • Xuefeng Tan
    • 1
    • 2
  • Dejun Zhang
    • 3
  1. 1.College of Information and EngineeringSichuan Agricultural UniversityYa’anChina
  2. 2.The Lab of Agricultural Information Engineering, Sichuan Key LaboratoryYa’anChina
  3. 3.School of Geography and Information EngineeringChina University of GeosciencesWuhanChina

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