LSTM-based multi-PIR sensor information fusing for estimating the speed and position of pedestrians on green campus
by Yong Zhang; Lixin Zhao; Yansong Fang; Daoming Mu; Haiyan Zhang
International Journal of Sensor Networks (IJSNET), Vol. 43, No. 4, 2023

Abstract: Pyroelectric infrared (PIR) sensors are popular for pedestrian detection in green campus lighting systems due to their low power consumption, low cost, and ease of installation. However, these sensors still fail to detect important information such as pedestrians' speed and position, which are necessary for precise lighting control. This paper proposes a method to detect pedestrian speed and position by utilising fused information from network-connected PIR sensors. First, we collect and analyse the time-domain signal of the PIR sensor, aggregate the collected data according to the peak time sequence features, and perform feature enhancement operations on the collected data. The speed and position of pedestrians are then estimated by using the long short-term memory neural network. Finally, the effectiveness of the proposed method is validated through simulations and experiments, as demonstrated through improved success rates in identifying pedestrians' moving position and speed, satisfying real-time detection requirements for green campus management.

Online publication date: Mon, 08-Jan-2024

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