Classification of Customer Behavior in Store Using on 3D Convolutional Neural Networks

Abstract

Customer behaviors represent their interests in the products. In recent years, with the spread of security cameras, it has become possible to record the customer behaviors. However, it takes a great amount of time and effort to analyze the recorded customer behaviors manually. Therefore, automation of customer behavior classification is required. The conventional two-stream I3D composed of 3D convolutional neural network can classify the continuous behaviors of trimmed videos. We extend the two-stream I3D to classify the behaviors from untrimmed videos. Our experimental results show improvement of precision, recall, F-measure, and accuracy using majority decision method for behavior classification.