Counting People by Using Convolutional Neural Network and A PIR Array

Abstract

Counting the number of people is a common and basic computing operation in many applications. Most of the people counting techniques need a sensing device like camera and apply image processing methods to track pedestrians. However, counting people with cameras in private places raises a lot of security and privacy issues. The passive infra-red sensor (PIR) can detect the body temperature of the infrared and thus provides another promising solution. Although a single PIR can easily identify the passing situations (i.e., in or out) of a single person, the signals of a single PIR is not sufficient to identify the complex situations of multiple people. In the paper, we design a people counting device with a PIR array to detect the passing situations and generate data records with higher discriminability. In addition, we apply the machine learning classification methods including the CNN, the RBM+LR, Decision Tree, and NaiveBayes on the collected data records to identify the passing situations. To validate our design, we conduct experiments to study the feasibility and classification performance and explore the impact factors. The experimental results show that the CNN outperforms the other and achieves the best accuracy, i.e., about 92%. Also, the results show that the captured data records of the PIR array contain sufficient characteristics for identifying complex passing situations and the configuration of the PIR array including the sensor direction and the field of view (FOV) of a PIR modified by the metal tape can significantly impact the discriminability of the collected data.