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

Hugo Gomes, Nuno Redinha, Nuno Lavado and Mateus Mendes ()
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Hugo Gomes: Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes—Quinta da Nora, 3030-199 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
Mateus Mendes: Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes—Quinta da Nora, 3030-199 Coimbra, Portugal

Energies, 2022, vol. 15, issue 23, 1-17

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.

Keywords: real-time object counting; YOLO; edge AI; Jetson Nano (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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