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Counting People and Bicycles in Real Time Using YOLO on Jetson Nano

Author

Listed:
  • Hugo Gomes

    (Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes—Quinta da Nora, 3030-199 Coimbra, Portugal
    Geologic Information Systems, Rua Pero Vaz de Caminha, 99, R/C, 3030-200 Coimbra, Portugal)

  • Nuno Redinha

    (Geologic Information Systems, Rua Pero Vaz de Caminha, 99, R/C, 3030-200 Coimbra, Portugal)

  • Nuno Lavado

    (Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes—Quinta da Nora, 3030-199 Coimbra, Portugal
    Research Group on Sustainability Cities and Urban Intelligence (SUScita), Polytechnic Institute of Coimbra, Rua Pedro Nunes—Quinta da Nora, 3030-199 Coimbra, Portugal)

  • Mateus Mendes

    (Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes—Quinta da Nora, 3030-199 Coimbra, Portugal
    Institute of Systems and Robotics, University of Coimbra, Rua Silvio Lima- Polo II, 3030-290 Coimbra, Portugal)

Abstract

Counting objects in video images has been an active area of computer vision for decades. For precise counting, it is necessary to detect objects and follow them through consecutive frames. Deep neural networks have allowed great improvements in this area. Nonetheless, this task is still a challenge for edge computing, especially when low-power edge AI devices must be used. The present work describes an application where an edge device is used to run a YOLO network and V-IOU tracker to count people and bicycles in real time. A selective frame-downsampling algorithm is used to allow a larger frame rate when necessary while optimizing memory usage and energy consumption. In the experiments, the system was able to detect and count the objects with 18 counting errors in 525 objects and a mean inference time of 112.82 ms per frame. With the selective downsampling algorithm, it was also capable of recovering and reduce memory usage while maintaining its precision.

Suggested Citation

  • Hugo Gomes & Nuno Redinha & Nuno Lavado & Mateus Mendes, 2022. "Counting People and Bicycles in Real Time Using YOLO on Jetson Nano," Energies, MDPI, vol. 15(23), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8816-:d:980872
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