Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Development of a Real-Time Automatic Passenger Counting System using Head Detection Based on Deep Learning

  • Kim, Hyunduk (Division of Automotive Technology, Daegu Gyeongbuk Institute of Science & Technology) ;
  • Sohn, Myoung-Kyu (Division of Automotive Technology, Daegu Gyeongbuk Institute of Science & Technology) ;
  • Lee, Sang-Heon (Division of Automotive Technology, Daegu Gyeongbuk Institute of Science & Technology)
  • Received : 2021.02.03
  • Accepted : 2022.01.08
  • Published : 2022.06.30
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Abstract

A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficient scheduling and management of transport routes. In this study, we introduce a lightweight head detection network using deep learning applicable to an embedded system. Currently, object detection algorithms using deep learning have been found to be successful. However, these algorithms essentially need a graphics processing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network using certain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using the proposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK 710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiency of the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system, we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances of exiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite the fact that only the CPU was used.

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References

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