TY - GEN
T1 - Towards a low-cost embedded vision-based occupancy recognition system for energy management applications
AU - Picon, German
AU - Benitez, Diego S.
AU - Perez, Noel
AU - Riofrio, Daniel
AU - Moyano, Ricardo Flores
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper focuses on the development of a low-cost real-time occupancy detection system for people using convolutional neural networks. The proposed detector was implemented in an embedded system composed of a Raspberry Pi 3, an Intel neural computer stick accelerator, and a control circuit containing a relay, a transistor, and the Raspberry output ports. The model was calibrated by varying two parameters: intersection-over-union score and probability size, both necessary to achieve high level of confidence when detecting a person. An experiment was carried out as proof of concept of the system under different test scenarios such as walking fast with poor and optimal lighting conditions and strolling with good lighting. As result, the system obtained a confidence level above the 80% on all test scenarios.
AB - This paper focuses on the development of a low-cost real-time occupancy detection system for people using convolutional neural networks. The proposed detector was implemented in an embedded system composed of a Raspberry Pi 3, an Intel neural computer stick accelerator, and a control circuit containing a relay, a transistor, and the Raspberry output ports. The model was calibrated by varying two parameters: intersection-over-union score and probability size, both necessary to achieve high level of confidence when detecting a person. An experiment was carried out as proof of concept of the system under different test scenarios such as walking fast with poor and optimal lighting conditions and strolling with good lighting. As result, the system obtained a confidence level above the 80% on all test scenarios.
KW - Deep neural networks
KW - IoU
KW - Neural computer stick (NCS2)
KW - Python
KW - Raspberry Pi
KW - Tiny-YOLO
UR - https://www.scopus.com/pages/publications/85126926269
U2 - 10.1109/CHILECON54041.2021.9702956
DO - 10.1109/CHILECON54041.2021.9702956
M3 - Contribución a la conferencia
AN - SCOPUS:85126926269
T3 - 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
BT - 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
Y2 - 6 December 2021 through 9 December 2021
ER -